fregu856 / Papers
Summaries of papers on machine learning, computer vision, autonomous robots etc.
Stars: ✭ 271
Projects that are alternatives of or similar to Papers
3d Pointcloud
Papers and Datasets about Point Cloud.
Stars: ✭ 179 (-33.95%)
Mutual labels: autonomous-driving, papers
Dataset Api
The ApolloScape Open Dataset for Autonomous Driving and its Application.
Stars: ✭ 260 (-4.06%)
Mutual labels: autonomous-driving
l2r
Open-source reinforcement learning environment for autonomous racing.
Stars: ✭ 38 (-85.98%)
Mutual labels: autonomous-driving
uncertainty-calibration
A collection of research and application papers of (uncertainty) calibration techniques.
Stars: ✭ 120 (-55.72%)
Mutual labels: papers
FusionAD
An open source autonomous driving stack by San Jose State University Autonomous Driving Team
Stars: ✭ 30 (-88.93%)
Mutual labels: autonomous-driving
papers-as-modules
Software Papers as Software Modules: Towards a Culture of Reusable Results
Stars: ✭ 18 (-93.36%)
Mutual labels: papers
Awesome-3D-Object-Detection-for-Autonomous-Driving
Papers on 3D Object Detection for Autonomous Driving
Stars: ✭ 52 (-80.81%)
Mutual labels: autonomous-driving
3dod thesis
3D Object Detection for Autonomous Driving in PyTorch, trained on the KITTI dataset.
Stars: ✭ 265 (-2.21%)
Mutual labels: autonomous-driving
autoreparam
Automatic Reparameterisation of Probabilistic Programs
Stars: ✭ 29 (-89.3%)
Mutual labels: papers
sparse-scene-flow
This repo contains C++ code for sparse scene flow method.
Stars: ✭ 23 (-91.51%)
Mutual labels: autonomous-driving
WIMP
[arXiv] What-If Motion Prediction for Autonomous Driving ❓🚗💨
Stars: ✭ 80 (-70.48%)
Mutual labels: autonomous-driving
nuplan-devkit
The devkit of the nuPlan dataset.
Stars: ✭ 107 (-60.52%)
Mutual labels: autonomous-driving
rail marking
proof-of-concept program that detects rail-track with semantic segmentation for autonomous train system
Stars: ✭ 21 (-92.25%)
Mutual labels: autonomous-driving
neat
[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving
Stars: ✭ 194 (-28.41%)
Mutual labels: autonomous-driving
Top Blockchain Paper
Top Blockchain paper, such as CCS, NSDI, S&P, EuroS&P, CRYPTO, etc.
Stars: ✭ 259 (-4.43%)
Mutual labels: papers
ml4se
A curated list of papers, theses, datasets, and tools related to the application of Machine Learning for Software Engineering
Stars: ✭ 46 (-83.03%)
Mutual labels: papers
VOS-Paper-List
Semi-Supervised Video Object Segmentation(VOS) Paper List
Stars: ✭ 28 (-89.67%)
Mutual labels: papers
Error-State-Extended-Kalman-Filter
Vehicle State Estimation using Error-State Extended Kalman Filter
Stars: ✭ 100 (-63.1%)
Mutual labels: autonomous-driving
Smoke
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
Stars: ✭ 266 (-1.85%)
Mutual labels: autonomous-driving
Literaturedl4graph
A comprehensive collection of recent papers on graph deep learning
Stars: ✭ 2,889 (+966.05%)
Mutual labels: papers
About
Summaries of papers I have read during my time as a PhD student.
The /commented_pdfs folder contains pdfs with comments, highlights etc. (visible at least in Okular on Ubuntu) for all papers.
Index
- All Papers
- Uncertainty Estimation
- Theoretical Properties of Deep Learning
- VAEs
- Normalizing Flows
- Autonomous Driving
- Medical Imaging
- Object Detection
- 3D Object Detection
- 3D Multi-Object Tracking
- Visual Tracking
- Sequence Modeling
- Reinforcement Learning
- System Identification
- Energy-Based Models
- Neural Processes
- SysCon Deep Learning Reading Group
- SysCon Monte Carlo Reading Group
- Papers by Year
- NeurIPS
- ICML
- ICLR
- CVPR
- ECCV
- AISTATS
- AAAI
- CDC
- JMLR
All Papers:
Papers Read in 2020:
[20-10-16] [paper108]
- Implicit Gradient Regularization [pdf] [pdf with comments] [comments]
- David G.T. Barrett, Benoit Dherin
2020-09-23
- [Theoretical Properties of Deep Learning]
[20-10-09] [paper107]
- Satellite Conjunction Analysis and the False Confidence Theorem [pdf] [pdf with comments] [comments]
- Michael Scott Balch, Ryan Martin, Scott Ferson
2018-03-21
[20-09-24] [paper106]
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness [pdf] [pdf with comments] [comments]
- Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020-06-17, NeurIPS 2020
- [Uncertainty Estimation]
[20-09-21] [paper105]
- Uncertainty Estimation Using a Single Deep Deterministic Neural Network [pdf] [code] [pdf with comments] [comments]
- Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020-03-04, ICML 2020
- [Uncertainty Estimation]
[20-09-11] [paper104]
- Gated Linear Networks [pdf] [pdf with comments] [comments]
- Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter
2020-06-11
[20-09-04] [paper103]
- Denoising Diffusion Probabilistic Models [pdf] [code] [pdf with comments] [comments]
- Jonathan Ho, Ajay Jain, Pieter Abbeel
20-06-19
- [Energy-Based Models]
[20-06-18] [paper102]
- Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[20-06-12] [paper101]
- End-to-End Object Detection with Transformers [pdf] [code] [pdf with comments] [comments]
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
2020-05-26, ECCV 2020
- [Object Detection]
[20-06-05] [paper100]
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
[20-05-27] [paper99]
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[20-05-10] [paper98]
- Stable Neural Flows [pdf] [pdf with comments] [comments]
- Stefano Massaroli, Michael Poli, Michelangelo Bin, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
2020-03-18
[20-04-17] [paper97]
- How Good is the Bayes Posterior in Deep Neural Networks Really? [pdf] [pdf with comments] [comments]
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020-02-06
- [Uncertainty Estimation] [Stochastic Gradient MCMC]
[20-04-09] [paper96]
- Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration [pdf] [code] [poster] [slides] [video] [pdf with comments] [comments]
- Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
2019-10-28, NeurIPS 2019
- [Uncertainty Estimation]
[20-04-03] [paper95]
- Normalizing Flows: An Introduction and Review of Current Methods [pdf] [pdf with comments] [comments]
- Ivan Kobyzev, Simon Prince, Marcus A. Brubaker
2019-08-25
- [Normalizing Flows]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-03-26] [paper93]
- Conservative Uncertainty Estimation By Fitting Prior Networks [pdf] [pdf with comments] [comments]
- Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
2019-10-25, ICLR 2020
- [Uncertainty Estimation]
[20-03-09] [paper92]
- Batch Normalization Biases Deep Residual Networks Towards Shallow Paths [pdf] [pdf with comments] [comments]
- Soham De, Samuel L. Smith
2020-02-24
- [Theoretical Properties of Deep Learning]
[20-02-28] [paper91]
- Bayesian Deep Learning and a Probabilistic Perspective of Generalization [pdf] [code] [pdf with comments] [comments]
- Andrew Gordon Wilson, Pavel Izmailov
2020-02-20
- [Uncertainty Estimation] [Ensembling]
[20-02-21] [paper90]
- Convolutional Conditional Neural Processes [pdf] [code] [pdf with comments] [comments]
- Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019-10-29, ICLR 2020
- [Neural Processes]
[20-02-18] [paper89]
- Probabilistic 3D Multi-Object Tracking for Autonomous Driving [pdf] [code] [pdf with comments] [comments]
- Hsu-kuang Chiu, Antonio Prioletti, Jie Li, Jeannette Bohg
2020-01-16
- [3D Multi-Object Tracking]
[20-02-15] [paper88]
- A Baseline for 3D Multi-Object Tracking [pdf] [code] [pdf with comments] [comments]
- Xinshuo Weng, Kris Kitani
2019-07-09
- [3D Multi-Object Tracking]
[20-02-14] [paper87]
- A Contrastive Divergence for Combining Variational Inference and MCMC [pdf] [code] [slides] [pdf with comments] [comments]
- Francisco J. R. Ruiz, Michalis K. Titsias
2019-05-10, ICML 2019
- [VAEs]
[20-02-13] [paper86]
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[20-02-08] [paper85]
- Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-06-26
- [Uncertainty Estimation] [Reinforcement Learning]
[20-01-31] [paper84]
- Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians [pdf] [code] [video] [pdf with comments] [comments]
- Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
2019-10-27, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-24] [paper83]
- A Primal-Dual link between GANs and Autoencoders [pdf] [poster] [pdf with comments] [comments]
- Hisham Husain, Richard Nock, Robert C. Williamson
2019-04-26, NeurIPS 2019
- [Theoretical Properties of Deep Learning]
[20-01-20] [paper82]
- A Connection Between Score Matching and Denoising Autoencoders [pdf] [pdf with comments] [comments]
- Pascal Vincent
2010-12
- [Energy-Based Models]
[20-01-17] [paper81]
- Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[20-01-16] [paper80]
- Estimation of Non-Normalized Statistical Models by Score Matching [pdf] [pdf with comments] [comments]
- Aapo Hyvärinen
2004-11, JMLR 6
- [Energy-Based Models]
[20-01-15] [paper79]
- Generative Modeling by Estimating Gradients of the Data Distribution [pdf] [code] [poster] [pdf with comments] [comments]
- Yang Song, Stefano Ermon
2019-07-12, NeurIPS 2019
- [Energy-Based Models]
[20-01-14] [paper78]
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models [pdf] [pdf with comments] [comments]
- Michael Gutmann, Aapo Hyvärinen
2009, AISTATS 2010
- [Energy-Based Models]
[20-01-10] [paper77]
- Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
[20-01-08] [paper76]
- Practical Deep Learning with Bayesian Principles [pdf] [code] [pdf with comments] [comments]
- Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019-06-06, NeurIPS 2019
- [Uncertainty Estimation] [Variational Inference]
[20-01-06] [paper75]
- Maximum Entropy Generators for Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, Yoshua Bengio
2019-01-24
- [Energy-Based Models]
Papers Read in 2019:
[19-12-22] [paper74]
- Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One [pdf] [pdf with comments] [comments]
- Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019-12-06, ICLR 2020
- [Energy-Based Models]
[19-12-20] [paper73]
- Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency [pdf] [pdf with comments] [comments]
- Zhuang Ma, Michael Collins
2018-09-06, EMNLP 2018
- [Energy-Based Models]
[19-12-20] [paper72]
- Flow Contrastive Estimation of Energy-Based Models [pdf] [pdf with comments] [comments]
- Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019-12-02, CVPR 2020
- [Energy-Based Models] [Normalizing Flows]
[19-12-19] [paper71]
- On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
2019-04-29, AAAI 2020
- [Energy-Based Models]
[19-12-15] [paper70]
- Implicit Generation and Generalization in Energy-Based Models [pdf] [code] [blog] [pdf with comments] [comments]
- Yilun Du, Igor Mordatch
2019-04-20, NeurIPS 2019
- [Energy-Based Models]
[19-12-14] [paper69]
- Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model [pdf] [poster] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
2019-04-22, NeurIPS 2019
- [Energy-Based Models]
[19-12-13] [paper68]
- A Tutorial on Energy-Based Learning [pdf] [pdf with comments] [comments]
- Yann LeCun, Sumit Chopra, Raia Hadsell, Marc Aurelio Ranzato, Fu Jie Huang
2006-08-19
- [Energy-Based Models]
[19-11-29] [paper67]
- Dream to Control: Learning Behaviors by Latent Imagination [pdf] [webpage] [pdf with comments] [comments]
- Anonymous
2019-09
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-11-22] [paper65]
- Learning Latent Dynamics for Planning from Pixels [pdf] [code] [blog] [pdf with comments] [comments]
- Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018-11-12, ICML2019
[19-10-28] [paper64]
- Learning nonlinear state-space models using deep autoencoders [pdf] [pdf with comments] [comments]
- Daniele Masti, Alberto Bemporad
2018, CDC2018
[19-10-18] [paper63]
- Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
- Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016-06-15, NeurIPS2016
[19-10-11] [paper62]
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
[19-10-04] [paper61]
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
[19-07-11] [paper60]
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
[19-07-10] [paper59]
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
[19-07-03] [paper58]
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
[19-06-12] [paper57]
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
[19-06-12] [paper56]
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[19-05-29] [paper54]
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
[19-04-05] [paper53]
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
[19-03-29] [paper52]
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
[19-03-15] [paper51]
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
[19-03-11] [paper50]
- Auto-Encoding Variational Bayes [pdf] [pdf with comments (TODO!)] [comments (TOOD!)]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
[19-03-04] [paper49]
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
[19-03-01] [paper48]
- Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
[19-02-27] [paper47]
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
[19-02-25] [paper46]
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
[19-02-22] [paper45]
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
[19-02-17] [paper44]
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
[19-02-14] [paper43]
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
[19-02-13] [paper42]
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
[19-02-12] [paper41]
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
[19-02-07] [paper40]
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
[19-02-06] [paper39]
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[19-01-28] [paper37]
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
[19-01-27] [paper36]
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
[19-01-26] [paper35]
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
[19-01-25] [paper34]
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
[19-01-25] [paper33]
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
[19-01-24] [paper32]
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
[19-01-24] [paper31]
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
[19-01-23] [paper30]
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
[19-01-23] [paper29]
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
[19-01-17] [paper28]
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
[19-01-09] [paper27]
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
Papers Read in 2018:
[18-12-12] [paper26]
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
[18-12-06] [paper25]
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
[18-12-05] [paper24]
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
[18-11-29] [paper23]
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-11-22] [paper21]
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
[18-11-15] [paper19]
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
[18-11-12] [paper18]
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
[18-11-08] [paper17]
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
[18-10-26] [paper16]
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
[18-10-25] [paper15]
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
[18-10-19] [paper14]
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
[18-10-18] [paper12]
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
[18-10-05] [paper11]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
[18-10-04] [paper10]
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
[18-10-04] [paper9]
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
[18-09-30] [paper8]
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
[18-09-27] [paper7]
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
[18-09-27] [paper6]
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
[18-09-25] [paper5]
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
[18-09-25] [paper4]
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
[18-09-24] [paper3]
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
[18-09-24] [paper2]
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
[18-09-20] [paper1]
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
Uncertainty Estimation:
[20-09-24] [paper106]
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness [pdf] [pdf with comments] [comments]
- Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020-06-17, NeurIPS 2020
- [Uncertainty Estimation]
[20-09-21] [paper105]
- Uncertainty Estimation Using a Single Deep Deterministic Neural Network [pdf] [code] [pdf with comments] [comments]
- Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020-03-04, ICML 2020
- [Uncertainty Estimation]
[20-06-05] [paper100]
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
[20-05-27] [paper99]
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[20-04-17] [paper97]
- How Good is the Bayes Posterior in Deep Neural Networks Really? [pdf] [pdf with comments] [comments]
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020-02-06
- [Uncertainty Estimation] [Stochastic Gradient MCMC]
[20-04-09] [paper96]
- Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration [pdf] [code] [poster] [slides] [video] [pdf with comments] [comments]
- Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
2019-10-28, NeurIPS 2019
- [Uncertainty Estimation]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-03-26] [paper93]
- Conservative Uncertainty Estimation By Fitting Prior Networks [pdf] [pdf with comments] [comments]
- Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
2019-10-25, ICLR 2020
- [Uncertainty Estimation]
[20-02-28] [paper91]
- Bayesian Deep Learning and a Probabilistic Perspective of Generalization [pdf] [code] [pdf with comments] [comments]
- Andrew Gordon Wilson, Pavel Izmailov
2020-02-20
- [Uncertainty Estimation] [Ensembling]
[20-02-13] [paper86]
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[20-02-08] [paper85]
- Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-06-26
- [Uncertainty Estimation] [Reinforcement Learning]
[20-01-31] [paper84]
- Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians [pdf] [code] [video] [pdf with comments] [comments]
- Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
2019-10-27, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-08] [paper76]
- Practical Deep Learning with Bayesian Principles [pdf] [code] [pdf with comments] [comments]
- Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019-06-06, NeurIPS 2019
- [Uncertainty Estimation] [Variational Inference]
[19-06-12] [paper56]
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[19-04-05] [paper53]
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
[19-02-27] [paper47]
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
[19-02-25] [paper46]
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
[19-02-14] [paper43]
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
[19-02-13] [paper42]
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
[19-02-12] [paper41]
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
[19-02-07] [paper40]
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
[19-02-06] [paper39]
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[19-01-28] [paper37]
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
[19-01-27] [paper36]
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
[19-01-26] [paper35]
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
[19-01-25] [paper34]
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
[19-01-25] [paper33]
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
[19-01-24] [paper32]
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
[19-01-23] [paper30]
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
[19-01-23] [paper29]
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
[19-01-09] [paper27]
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
[18-12-06] [paper25]
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
[18-12-05] [paper24]
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
[18-11-29] [paper23]
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
[18-11-12] [paper18]
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
[18-10-26] [paper16]
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
[18-10-19] [paper14]
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
[18-10-18] [paper12]
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
[18-09-25] [paper5]
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
[18-09-25] [paper4]
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
[18-09-24] [paper3]
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
[18-09-24] [paper2]
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
Theoretical Properties of Deep Learning:
[20-10-16] [paper108]
- Implicit Gradient Regularization [pdf] [pdf with comments] [comments]
- David G.T. Barrett, Benoit Dherin
2020-09-23
- [Theoretical Properties of Deep Learning]
[20-03-09] [paper92]
- Batch Normalization Biases Deep Residual Networks Towards Shallow Paths [pdf] [pdf with comments] [comments]
- Soham De, Samuel L. Smith
2020-02-24
- [Theoretical Properties of Deep Learning]
[20-01-24] [paper83]
- A Primal-Dual link between GANs and Autoencoders [pdf] [poster] [pdf with comments] [comments]
- Hisham Husain, Richard Nock, Robert C. Williamson
2019-04-26, NeurIPS 2019
- [Theoretical Properties of Deep Learning]
[20-01-17] [paper81]
- Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[19-02-22] [paper45]
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
[19-02-17] [paper44]
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
[19-01-17] [paper28]
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
[18-11-08] [paper17]
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
[18-10-25] [paper15]
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
[18-09-20] [paper1]
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
VAEs:
[20-06-18] [paper102]
- Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[20-02-14] [paper87]
- A Contrastive Divergence for Combining Variational Inference and MCMC [pdf] [code] [slides] [pdf with comments] [comments]
- Francisco J. R. Ruiz, Michalis K. Titsias
2019-05-10, ICML 2019
- [VAEs]
[20-01-10] [paper77]
- Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-03-11] [paper50]
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
[19-03-04] [paper49]
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
Normalizing Flows:
[20-04-03] [paper95]
- Normalizing Flows: An Introduction and Review of Current Methods [pdf] [pdf with comments] [comments]
- Ivan Kobyzev, Simon Prince, Marcus A. Brubaker
2019-08-25
- [Normalizing Flows]
[19-12-20] [paper72]
- Flow Contrastive Estimation of Energy-Based Models [pdf] [pdf with comments] [comments]
- Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019-12-02, CVPR 2020
- [Energy-Based Models] [Normalizing Flows]
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-10-18] [paper63]
- Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
- Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016-06-15, NeurIPS2016
[19-10-11] [paper62]
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
[18-09-27] [paper6]
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
Autonomous Driving:
[19-07-11] [paper60]
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
[19-07-10] [paper59]
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
[19-07-03] [paper58]
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[19-01-09] [paper27]
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
[18-12-06] [paper25]
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
[18-10-26] [paper16]
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
[18-10-05] [paper11]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
[18-10-04] [paper10]
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
[18-09-25] [paper4]
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
[18-09-24] [paper2]
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
Medical Imaging:
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
Object Detection:
[20-06-12] [paper101]
- End-to-End Object Detection with Transformers [pdf] [code] [pdf with comments] [comments]
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
2020-05-26, ECCV 2020
- [Object Detection]
[19-07-03] [paper58]
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
[19-06-12] [paper56]
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
3D Object Detection:
[19-07-11] [paper60]
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
[19-07-10] [paper59]
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
[19-07-03] [paper58]
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[18-10-26] [paper16]
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
[18-10-05] [paper11]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
[18-10-04] [paper10]
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
[18-09-25] [paper4]
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
3D Multi-Object Tracking:
[20-02-18] [paper89]
- Probabilistic 3D Multi-Object Tracking for Autonomous Driving [pdf] [code] [pdf with comments] [comments]
- Hsu-kuang Chiu, Antonio Prioletti, Jie Li, Jeannette Bohg
2020-01-16
- [3D Multi-Object Tracking]
[20-02-15] [paper88]
- A Baseline for 3D Multi-Object Tracking [pdf] [code] [pdf with comments] [comments]
- Xinshuo Weng, Kris Kitani
2019-07-09
- [3D Multi-Object Tracking]
Visual Tracking:
[19-06-12] [paper57]
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
Sequence Modeling:
[20-01-17] [paper81]
- Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[20-01-10] [paper77]
- Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-10-04] [paper61]
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
[19-05-29] [paper54]
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
[19-03-15] [paper51]
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
[19-01-24] [paper31]
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
[18-11-22] [paper21]
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
Reinforcement Learning:
[20-02-13] [paper86]
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[20-02-08] [paper85]
- Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-06-26
- [Uncertainty Estimation] [Reinforcement Learning]
[19-11-29] [paper67]
- Dream to Control: Learning Behaviors by Latent Imagination [pdf] [webpage] [pdf with comments] [comments]
- Anonymous
2019-09
[19-11-22] [paper65]
- Learning Latent Dynamics for Planning from Pixels [pdf] [code] [blog] [pdf with comments] [comments]
- Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018-11-12, ICML2019
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
System Identification:
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-10-28] [paper64]
- Learning nonlinear state-space models using deep autoencoders [pdf] [pdf with comments] [comments]
- Daniele Masti, Alberto Bemporad
2018, CDC2018
Energy-Based Models:
[20-09-04] [paper103]
- Denoising Diffusion Probabilistic Models [pdf] [code] [pdf with comments] [comments]
- Jonathan Ho, Ajay Jain, Pieter Abbeel
20-06-19
- [Energy-Based Models]
[20-06-18] [paper102]
- Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[20-01-20] [paper82]
- A Connection Between Score Matching and Denoising Autoencoders [pdf] [pdf with comments] [comments]
- Pascal Vincent
2010-12
- [Energy-Based Models]
[20-01-16] [paper80]
- Estimation of Non-Normalized Statistical Models by Score Matching [pdf] [pdf with comments] [comments]
- Aapo Hyvärinen
2004-11, JMLR 6
- [Energy-Based Models]
[20-01-15] [paper79]
- Generative Modeling by Estimating Gradients of the Data Distribution [pdf] [code] [poster] [pdf with comments] [comments]
- Yang Song, Stefano Ermon
2019-07-12, NeurIPS 2019
- [Energy-Based Models]
[20-01-14] [paper78]
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models [pdf] [pdf with comments] [comments]
- Michael Gutmann, Aapo Hyvärinen
2009, AISTATS 2010
- [Energy-Based Models]
[20-01-06] [paper75]
- Maximum Entropy Generators for Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, Yoshua Bengio
2019-01-24
- [Energy-Based Models]
[19-12-22] [paper74]
- Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One [pdf] [pdf with comments] [comments]
- Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019-12-06, ICLR 2020
- [Energy-Based Models]
[19-12-20] [paper73]
- Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency [pdf] [pdf with comments] [comments]
- Zhuang Ma, Michael Collins
2018-09-06, EMNLP 2018
- [Energy-Based Models]
[19-12-20] [paper72]
- Flow Contrastive Estimation of Energy-Based Models [pdf] [pdf with comments] [comments]
- Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019-12-02, CVPR 2020
- [Energy-Based Models] [Normalizing Flows]
[19-12-19] [paper71]
- On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
2019-04-29, AAAI 2020
- [Energy-Based Models]
[19-12-15] [paper70]
- Implicit Generation and Generalization in Energy-Based Models [pdf] [code] [blog] [pdf with comments] [comments]
- Yilun Du, Igor Mordatch
2019-04-20, NeurIPS 2019
- [Energy-Based Models]
[19-12-14] [paper69]
- Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model [pdf] [poster] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
2019-04-22, NeurIPS 2019
- [Energy-Based Models]
[19-12-13] [paper68]
- A Tutorial on Energy-Based Learning [pdf] [pdf with comments] [comments]
- Yann LeCun, Sumit Chopra, Raia Hadsell, Marc Aurelio Ranzato, Fu Jie Huang
2006-08-19
- [Energy-Based Models]
Ensembling:
[20-05-27] [paper99]
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-02-28] [paper91]
- Bayesian Deep Learning and a Probabilistic Perspective of Generalization [pdf] [code] [pdf with comments] [comments]
- Andrew Gordon Wilson, Pavel Izmailov
2020-02-20
- [Uncertainty Estimation] [Ensembling]
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
[18-11-12] [paper18]
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
[18-10-19] [paper14]
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
[18-10-18] [paper13]
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
[18-09-25] [paper5]
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
Stochastic Gradient MCMC:
[20-04-17] [paper97]
- How Good is the Bayes Posterior in Deep Neural Networks Really? [pdf] [pdf with comments] [comments]
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020-02-06
- [Uncertainty Estimation] [Stochastic Gradient MCMC]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[19-04-05] [paper53]
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
[19-02-13] [paper42]
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
[19-01-26] [paper35]
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
[19-01-25] [paper34]
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
[19-01-25] [paper33]
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
[19-01-24] [paper32]
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
[19-01-23] [paper30]
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
[19-01-23] [paper29]
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
Variational Inference:
[20-06-05] [paper100]
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
[20-01-08] [paper76]
- Practical Deep Learning with Bayesian Principles [pdf] [code] [pdf with comments] [comments]
- Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019-06-06, NeurIPS 2019
- [Uncertainty Estimation] [Variational Inference]
[19-02-07] [paper40]
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
[19-01-28] [paper37]
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
[19-01-27] [paper36]
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
Neural Processes:
[20-02-21] [paper90]
- Convolutional Conditional Neural Processes [pdf] [code] [pdf with comments] [comments]
- Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019-10-29, ICLR 2020
- [Neural Processes]
[18-09-30] [paper8]
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
[18-09-27] [paper7]
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
SysCon Deep Learning Reading Group:
(Current paper selection order: Calle --> Carmen --> Daniel --> David --> Fredrik --> John --> Calle --> ...)
Reading Group Papers in 2020:
[20-10-16] [paper108]
[2020 w.42] - Implicit Gradient Regularization [pdf] [pdf with comments] [comments]
- David G.T. Barrett, Benoit Dherin
2020-09-23
- [Theoretical Properties of Deep Learning]
[20-10-09] [paper107]
[2020 w.41] - Satellite Conjunction Analysis and the False Confidence Theorem [pdf] [pdf with comments] [comments]
- Michael Scott Balch, Ryan Martin, Scott Ferson
2018-03-21
[20-09-24] [paper106]
[2020 w.39] - Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness [pdf] [pdf with comments] [comments]
- Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020-06-17, NeurIPS 2020
- [Uncertainty Estimation]
[20-09-21] [paper105]
[2020 w.38] - Uncertainty Estimation Using a Single Deep Deterministic Neural Network [pdf] [code] [pdf with comments] [comments]
- Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020-03-04, ICML 2020
- [Uncertainty Estimation]
[20-09-11] [paper104]
[2020 w.37] - Gated Linear Networks [pdf] [pdf with comments] [comments]
- Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter
2020-06-11
[20-09-04] [paper103]
[2020 w.36] - Denoising Diffusion Probabilistic Models [pdf] [code] [pdf with comments] [comments]
- Jonathan Ho, Ajay Jain, Pieter Abbeel
20-06-19
- [Energy-Based Models]
[20-06-18] [paper102]
[2020 w.25] - Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[20-06-12] [paper101]
[2020 w.24] - End-to-End Object Detection with Transformers [pdf] [code] [pdf with comments] [comments]
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
2020-05-26, ECCV 2020
- [Object Detection]
[20-06-05] [paper100]
[2020 w.23] - Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
[20-05-27] [paper99]
[2020 w.22] - BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[19-12-22] [paper74]
[2020 w.20] - Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One [pdf] [pdf with comments] [comments]
- Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019-12-06, ICLR 2020
- [Energy-Based Models]
[20-05-10] [paper98]
[2020 w.19] - Stable Neural Flows [pdf] [pdf with comments] [comments]
- Stefano Massaroli, Michael Poli, Michelangelo Bin, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
2020-03-18
[20-04-17] [paper97]
[2020 w.16] - How Good is the Bayes Posterior in Deep Neural Networks Really? [pdf] [pdf with comments] [comments]
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020-02-06
- [Uncertainty Estimation] [Stochastic Gradient MCMC]
[20-04-09] [paper96]
[2020 w.15] - Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration [pdf] [code] [poster] [slides] [video] [pdf with comments] [comments]
- Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
2019-10-28, NeurIPS 2019
- [Uncertainty Estimation]
[20-04-03] [paper95]
[2020 w.14] - Normalizing Flows: An Introduction and Review of Current Methods [pdf] [pdf with comments] [comments]
- Ivan Kobyzev, Simon Prince, Marcus A. Brubaker
2019-08-25
- [Normalizing Flows]
[20-03-27] [paper94]
[2020 w.13] - Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-03-26] [paper93]
[2020 w.12] - Conservative Uncertainty Estimation By Fitting Prior Networks [pdf] [pdf with comments] [comments]
- Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
2019-10-25, ICLR 2020
- [Uncertainty Estimation]
[20-03-09] [paper92]
[2020 w.10] - Batch Normalization Biases Deep Residual Networks Towards Shallow Paths [pdf] [pdf with comments] [comments]
- Soham De, Samuel L. Smith
2020-02-24
- [Theoretical Properties of Deep Learning]
[20-02-28] [paper91]
[2020 w.9] - Bayesian Deep Learning and a Probabilistic Perspective of Generalization [pdf] [code] [pdf with comments] [comments]
- Andrew Gordon Wilson, Pavel Izmailov
2020-02-20
- [Uncertainty Estimation] [Ensembling]
[20-02-21] [paper90]
[2020 w.8] - Convolutional Conditional Neural Processes [pdf] [code] [pdf with comments] [comments]
- Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019-10-29, ICLR 2020
- [Neural Processes]
[20-02-13] [paper86]
[2020 w.7] - Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[20-02-08] [paper85]
[2020 w.6] - Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-06-26
- [Uncertainty Estimation] [Reinforcement Learning]
[20-01-31] [paper84]
[2020 w.5] - Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians [pdf] [code] [video] [pdf with comments] [comments]
- Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
2019-10-27, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-24] [paper83]
[2020 w.4] - A Primal-Dual link between GANs and Autoencoders [pdf] [poster] [pdf with comments] [comments]
- Hisham Husain, Richard Nock, Robert C. Williamson
2019-04-26, NeurIPS 2019
- [Theoretical Properties of Deep Learning]
[20-01-17] [paper81]
[2020 w.3] - Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[20-01-10] [paper77]
[2020 w.2] - Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
Reading Group Papers in 2019:
[19-11-29] [paper67]
[2019 w.48] - Dream to Control: Learning Behaviors by Latent Imagination [pdf] [webpage] [pdf with comments] [comments]
- Anonymous
2019-09
[19-11-22] [paper65]
[2019 w.47] - Learning Latent Dynamics for Planning from Pixels [pdf] [code] [blog] [pdf with comments] [comments]
- Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018-11-12, ICML2019
[19-10-28] [paper64]
[2019 w.46] - Learning nonlinear state-space models using deep autoencoders [pdf] [pdf with comments] [comments]
- Daniele Masti, Alberto Bemporad
2018, CDC2018
[19-01-27] [paper36]
[2019 w.45] - Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
[18-09-27] [paper6]
[2019 w.43] - Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
[19-10-18] [paper63]
[2019 w.42] - Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
- Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016-06-15, NeurIPS2016
[19-10-11] [paper62]
[2019 w.41] - Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
[19-10-04] [paper61]
[2019 w.40] - Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
[19-06-05] [paper55]
[2019 w.23] - LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[19-05-29] [paper54]
[2019 w.22] - Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
[19-02-17] [paper44]
[2019 w.18] - Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
[19-04-05] [paper53]
[2019 w.14] - Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
[19-03-29] [paper52]
[2019 w.13] - Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
[19-02-25] [paper46]
[2019 w.12] - Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
[19-03-15] [paper51]
[2019 w.11] - A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
[19-03-04] [paper49]
[2019 w.10] - Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
[19-03-01] [paper48]
[2019 w.9] - Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
[19-02-22] [paper45]
[2019 w.8] - Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
[19-02-14] [paper43]
[2019 w.7] - A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
[19-02-05] [paper38]
[2019 w.6] - Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[19-01-25] [paper33]
[2019 w.5] - A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
[19-01-24] [paper31]
[2019 w.4] - An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
[19-01-17] [paper28]
[2019 w.3] - How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
[18-09-30] [paper8]
[2019 w.2] - Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
Reading Group Papers in 2018:
[18-12-12] [paper26]
[2018 w.50] - Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
[18-11-29] [paper23]
[2018 w.49] - Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
[18-11-22] [paper22]
[2018 w.48] - A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-11-22] [paper21]
[2018 w.47] - When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
[18-11-15] [paper19]
[2018 w.46] - Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
[18-11-08] [paper17]
[2018 w.45] - The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
[18-09-27] [paper7]
[2018 w.44] - Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
[18-10-25] [paper15]
[2018 w.43] - Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
[18-10-04] [paper9]
[2018 w.41] - On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
[18-09-27] [paper6]
[2018 w.39] - Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
[18-09-20] [paper1]
[2018 w.38] - Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
SysCon Monte Carlo Reading Group:
[2019 w.6 II]
- The Continuous-Discrete Time Feedback Particle Filter [pdf]
- Tao Yang, Henk A. P. Blom, Prashant G. Mehta
2014, American Control Conference
[2019 w.6 I]
- Feedback Particle Filter [pdf]
- Tao Yang, Prashant G. Mehta, Sean P. Meyn
2013, IEEE Transactions on Automatic Control
[2019 w.3]
- Markov Chains for Exploring Posterior Distributions [pdf] [pdf with comments]
- Luke Tierney
1994-12, The Annals of Statistics
[2018 w.50 II]
- Particle Gibbs with Ancestor Sampling [pdf]
- Fredrik Lindsten, Michael I. Jordan, Thomas B. Schön
2014-06-14, Journal of Machine Learning Research
[2018 w.50 I]
- Particle Markov chain Monte Carlo methods [pdf]
- Christophe Andrieu, Arnaud Doucet, Roman Holenstein
2010, Journal of the Royal Statistical Society
[2018 w.48]
- State Space LSTM Models with Particle MCMC Inference [pdf]
- Xun Zheng, Manzil Zaheer, Amr Ahmed, Yuan Wang, Eric P Xing, Alexander J Smola
2017-11-30
[2018 w.46]
- Rethinking the Effective Sample Size [pdf]
- Víctor Elvira, Luca Martino, Christian P. Robert
- `2018-09-11,
NeurIPS:
NeurIPS 2020:
[20-09-24] [paper106]
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness [pdf] [pdf with comments] [comments]
- Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020-06-17, NeurIPS 2020
- [Uncertainty Estimation]
NeurIPS 2019:
[20-04-09] [paper96]
- Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration [pdf] [code] [poster] [slides] [video] [pdf with comments] [comments]
- Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
2019-10-28, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-31] [paper84]
- Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians [pdf] [code] [video] [pdf with comments] [comments]
- Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
2019-10-27, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-24] [paper83]
- A Primal-Dual link between GANs and Autoencoders [pdf] [poster] [pdf with comments] [comments]
- Hisham Husain, Richard Nock, Robert C. Williamson
2019-04-26, NeurIPS 2019
- [Theoretical Properties of Deep Learning]
[20-01-15] [paper79]
- Generative Modeling by Estimating Gradients of the Data Distribution [pdf] [code] [poster] [pdf with comments] [comments]
- Yang Song, Stefano Ermon
2019-07-12, NeurIPS 2019
- [Energy-Based Models]
[20-01-08] [paper76]
- Practical Deep Learning with Bayesian Principles [pdf] [code] [pdf with comments] [comments]
- Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019-06-06, NeurIPS 2019
- [Uncertainty Estimation] [Variational Inference]
[19-12-15] [paper70]
- Implicit Generation and Generalization in Energy-Based Models [pdf] [code] [blog] [pdf with comments] [comments]
- Yilun Du, Igor Mordatch
2019-04-20, NeurIPS 2019
- [Energy-Based Models]
[19-12-14] [paper69]
- Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model [pdf] [poster] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
2019-04-22, NeurIPS 2019
- [Energy-Based Models]
NeurIPS 2018:
[19-03-04] [paper49]
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
[19-02-27] [paper47]
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
[19-02-17] [paper44]
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[19-01-17] [paper28]
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
[19-01-09] [paper27]
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
[18-12-12] [paper26]
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
[18-11-29] [paper23]
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-10-04] [paper9]
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
NeurIPS 2017:
[20-01-10] [paper77]
- Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
[19-05-29] [paper54]
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
[18-10-18] [paper13]
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
[18-09-24] [paper2]
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
NeurIPS 2016:
[19-10-18] [paper63]
- Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
- Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016-06-15, NeurIPS2016
NeurIPS 2015:
[19-02-12] [paper41]
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
[19-01-25] [paper33]
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
NeurIPS 2011:
[19-01-28] [paper37]
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
ICML:
ICML 2020:
[20-09-21] [paper105]
- Uncertainty Estimation Using a Single Deep Deterministic Neural Network [pdf] [code] [pdf with comments] [comments]
- Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020-03-04, ICML 2020
- [Uncertainty Estimation]
[20-06-05] [paper100]
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
ICML 2019:
[20-02-14] [paper87]
- A Contrastive Divergence for Combining Variational Inference and MCMC [pdf] [code] [slides] [pdf with comments] [comments]
- Francisco J. R. Ruiz, Michalis K. Titsias
2019-05-10, ICML 2019
- [VAEs]
[19-11-22] [paper65]
- Learning Latent Dynamics for Planning from Pixels [pdf] [code] [blog] [pdf with comments] [comments]
- Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018-11-12, ICML2019
ICML 2018:
[20-02-13] [paper86]
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[19-02-07] [paper40]
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
[18-11-15] [paper19]
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
[18-10-18] [paper12]
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
[18-09-30] [paper8]
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
[18-09-27] [paper7]
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
[18-09-27] [paper6]
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
ICML 2017:
[18-12-05] [paper24]
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
ICML 2015:
[19-10-11] [paper62]
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
[19-02-06] [paper39]
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
[19-01-27] [paper36]
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
ICML 2014:
[19-01-23] [paper30]
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
ICML 2011:
[19-01-23] [paper29]
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
ICLR:
ICLR 2020:
[20-05-27] [paper99]
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-03-26] [paper93]
- Conservative Uncertainty Estimation By Fitting Prior Networks [pdf] [pdf with comments] [comments]
- Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
2019-10-25, ICLR 2020
- [Uncertainty Estimation]
[20-02-21] [paper90]
- Convolutional Conditional Neural Processes [pdf] [code] [pdf with comments] [comments]
- Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019-10-29, ICLR 2020
- [Neural Processes]
[20-01-17] [paper81]
- Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[19-12-22] [paper74]
- Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One [pdf] [pdf with comments] [comments]
- Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019-12-06, ICLR 2020
- [Energy-Based Models]
ICLR 2019:
[19-10-04] [paper61]
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
[19-03-29] [paper52]
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
[19-01-25] [paper34]
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
[18-11-22] [paper21]
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
[18-11-08] [paper17]
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
[18-10-25] [paper15]
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
ICLR 2018:
[18-09-20] [paper1]
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
ICLR 2017:
[19-03-15] [paper51]
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
ICLR 2014:
[19-03-11] [paper50]
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
CVPR:
CVPR 2020:
[20-06-18] [paper102]
- Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[19-12-20] [paper72]
- Flow Contrastive Estimation of Energy-Based Models [pdf] [pdf with comments] [comments]
- Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019-12-02, CVPR 2020
- [Energy-Based Models] [Normalizing Flows]
CVPR 2019:
[19-07-10] [paper59]
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
[19-06-12] [paper57]
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
CVPR 2018:
[18-10-05] [paper11]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
[18-10-04] [paper10]
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
[18-09-24] [paper3]
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
CVPR 2016:
[19-01-26] [paper35]
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
ECCV:
ECCV 2020:
[20-06-12] [paper101]
- End-to-End Object Detection with Transformers [pdf] [code] [pdf with comments] [comments]
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
2020-05-26, ECCV 2020
- [Object Detection]
ECCV 2018:
[19-06-12] [paper56]
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
AISTATS:
AISTATS 2019:
[19-02-25] [paper46]
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS 2019
AISTATS 2010:
[20-01-14] [paper78]
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models [pdf] [pdf with comments] [comments]
- Michael Gutmann, Aapo Hyvärinen
2009, AISTATS 2010
- [Energy-Based Models]
AAAI:
AAAI 2020:
[19-12-19] [paper71]
- On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
2019-04-29, AAAI 2020
- [Energy-Based Models]
CDC:
CDC 2018:
[19-10-28] [paper64]
- Learning nonlinear state-space models using deep autoencoders [pdf] [pdf with comments] [comments]
- Daniele Masti, Alberto Bemporad
2018, CDC2018
JMLR:
[20-01-16] [paper80]
- Estimation of Non-Normalized Statistical Models by Score Matching [pdf] [pdf with comments] [comments]
- Aapo Hyvärinen
2004-11, JMLR 6
- [Energy-Based Models]
Papers by Year:
2020:
[20-10-16] [paper108]
- Implicit Gradient Regularization [pdf] [pdf with comments] [comments]
- David G.T. Barrett, Benoit Dherin
2020-09-23
- [Theoretical Properties of Deep Learning]
[20-09-24] [paper106]
- Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness [pdf] [pdf with comments] [comments]
- Jeremiah Zhe Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
2020-06-17, NeurIPS 2020
- [Uncertainty Estimation]
[20-09-21] [paper105]
- Uncertainty Estimation Using a Single Deep Deterministic Neural Network [pdf] [code] [pdf with comments] [comments]
- Joost van Amersfoort, Lewis Smith, Yee Whye Teh, Yarin Gal
2020-03-04, ICML 2020
- [Uncertainty Estimation]
[20-09-11] [paper104]
- Gated Linear Networks [pdf] [pdf with comments] [comments]
- Joel Veness, Tor Lattimore, David Budden, Avishkar Bhoopchand, Christopher Mattern, Agnieszka Grabska-Barwinska, Eren Sezener, Jianan Wang, Peter Toth, Simon Schmitt, Marcus Hutter
2020-06-11
[20-09-04] [paper103]
- Denoising Diffusion Probabilistic Models [pdf] [code] [pdf with comments] [comments]
- Jonathan Ho, Ajay Jain, Pieter Abbeel
20-06-19
- [Energy-Based Models]
[20-06-18] [paper102]
- Joint Training of Variational Auto-Encoder and Latent Energy-Based Model [pdf] [code] [pdf with comments] [comments]
- Tian Han, Erik Nijkamp, Linqi Zhou, Bo Pang, Song-Chun Zhu, Ying Nian Wu
2020-06-10, CVPR 2020
- [VAEs] [Energy-Based Models]
[20-06-12] [paper101]
- End-to-End Object Detection with Transformers [pdf] [code] [pdf with comments] [comments]
- Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, Sergey Zagoruyko
2020-05-26, ECCV 2020
- [Object Detection]
[20-06-05] [paper100]
- Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors [pdf] [code] [pdf with comments] [comments]
- Michael W. Dusenberry, Ghassen Jerfel, Yeming Wen, Yi-an Ma, Jasper Snoek, Katherine Heller, Balaji Lakshminarayanan, Dustin Tran
2020-05-14, ICML 2020
- [Uncertainty Estimation] [Variational Inference]
[20-05-27] [paper99]
- BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning [pdf] [code] [video] [pdf with comments] [comments]
- Yeming Wen, Dustin Tran, Jimmy Ba
2020-02-17, ICLR 2020
- [Uncertainty Estimation] [Ensembling]
[20-05-10] [paper98]
- Stable Neural Flows [pdf] [pdf with comments] [comments]
- Stefano Massaroli, Michael Poli, Michelangelo Bin, Jinkyoo Park, Atsushi Yamashita, Hajime Asama
2020-03-18
[20-04-17] [paper97]
- How Good is the Bayes Posterior in Deep Neural Networks Really? [pdf] [pdf with comments] [comments]
- Florian Wenzel, Kevin Roth, Bastiaan S. Veeling, Jakub Świątkowski, Linh Tran, Stephan Mandt, Jasper Snoek, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
2020-02-06
- [Uncertainty Estimation] [Stochastic Gradient MCMC]
[20-03-27] [paper94]
- Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Arsenii Ashukha, Alexander Lyzhov, Dmitry Molchanov, Dmitry Vetrov
2020-02-15, ICLR 2020
- [Uncertainty Estimation] [Ensembling] [Stochastic Gradient MCMC]
[20-03-09] [paper92]
- Batch Normalization Biases Deep Residual Networks Towards Shallow Paths [pdf] [pdf with comments] [comments]
- Soham De, Samuel L. Smith
2020-02-24
- [Theoretical Properties of Deep Learning]
[20-02-28] [paper91]
- Bayesian Deep Learning and a Probabilistic Perspective of Generalization [pdf] [code] [pdf with comments] [comments]
- Andrew Gordon Wilson, Pavel Izmailov
2020-02-20
- [Uncertainty Estimation] [Ensembling]
[20-02-18] [paper89]
- Probabilistic 3D Multi-Object Tracking for Autonomous Driving [pdf] [code] [pdf with comments] [comments]
- Hsu-kuang Chiu, Antonio Prioletti, Jie Li, Jeannette Bohg
2020-01-16
- [3D Multi-Object Tracking]
2019:
[20-04-09] [paper96]
- Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration [pdf] [code] [poster] [slides] [video] [pdf with comments] [comments]
- Meelis Kull, Miquel Perello-Nieto, Markus Kängsepp, Telmo Silva Filho, Hao Song, Peter Flach
2019-10-28, NeurIPS 2019
- [Uncertainty Estimation]
[20-04-03] [paper95]
- Normalizing Flows: An Introduction and Review of Current Methods [pdf] [pdf with comments] [comments]
- Ivan Kobyzev, Simon Prince, Marcus A. Brubaker
2019-08-25
- [Normalizing Flows]
[20-03-26] [paper93]
- Conservative Uncertainty Estimation By Fitting Prior Networks [pdf] [pdf with comments] [comments]
- Kamil Ciosek, Vincent Fortuin, Ryota Tomioka, Katja Hofmann, Richard Turner
2019-10-25, ICLR 2020
- [Uncertainty Estimation]
[20-02-21] [paper90]
- Convolutional Conditional Neural Processes [pdf] [code] [pdf with comments] [comments]
- Jonathan Gordon, Wessel P. Bruinsma, Andrew Y. K. Foong, James Requeima, Yann Dubois, Richard E. Turner
2019-10-29, ICLR 2020
- [Neural Processes]
[20-02-15] [paper88]
- A Baseline for 3D Multi-Object Tracking [pdf] [code] [pdf with comments] [comments]
- Xinshuo Weng, Kris Kitani
2019-07-09
- [3D Multi-Object Tracking]
[20-02-14] [paper87]
- A Contrastive Divergence for Combining Variational Inference and MCMC [pdf] [code] [slides] [pdf with comments] [comments]
- Francisco J. R. Ruiz, Michalis K. Titsias
2019-05-10, ICML 2019
- [VAEs]
[20-01-31] [paper84]
- Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians [pdf] [code] [video] [pdf with comments] [comments]
- Axel Brando, Jose A. Rodríguez-Serrano, Jordi Vitrià, Alberto Rubio
2019-10-27, NeurIPS 2019
- [Uncertainty Estimation]
[20-01-24] [paper83]
- A Primal-Dual link between GANs and Autoencoders [pdf] [poster] [pdf with comments] [comments]
- Hisham Husain, Richard Nock, Robert C. Williamson
2019-04-26, NeurIPS 2019
- [Theoretical Properties of Deep Learning]
[20-01-17] [paper81]
- Multiplicative Interactions and Where to Find Them [pdf] [pdf with comments] [comments]
- Siddhant M. Jayakumar, Jacob Menick, Wojciech M. Czarnecki, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
2019-09-25, ICLR 2020
- [Theoretical Properties of Deep Learning] [Sequence Modeling]
[20-01-15] [paper79]
- Generative Modeling by Estimating Gradients of the Data Distribution [pdf] [code] [poster] [pdf with comments] [comments]
- Yang Song, Stefano Ermon
2019-07-12, NeurIPS 2019
- [Energy-Based Models]
[20-01-08] [paper76]
- Practical Deep Learning with Bayesian Principles [pdf] [code] [pdf with comments] [comments]
- Kazuki Osawa, Siddharth Swaroop, Anirudh Jain, Runa Eschenhagen, Richard E. Turner, Rio Yokota, Mohammad Emtiyaz Khan
2019-06-06, NeurIPS 2019
- [Uncertainty Estimation] [Variational Inference]
[20-01-06] [paper75]
- Maximum Entropy Generators for Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Rithesh Kumar, Sherjil Ozair, Anirudh Goyal, Aaron Courville, Yoshua Bengio
2019-01-24
- [Energy-Based Models]
[19-12-22] [paper74]
- Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One [pdf] [pdf with comments] [comments]
- Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky
2019-12-06, ICLR 2020
- [Energy-Based Models]
[19-12-20] [paper72]
- Flow Contrastive Estimation of Energy-Based Models [pdf] [pdf with comments] [comments]
- Ruiqi Gao, Erik Nijkamp, Diederik P. Kingma, Zhen Xu, Andrew M. Dai, Ying Nian Wu
2019-12-02, CVPR 2020
- [Energy-Based Models] [Normalizing Flows]
[19-12-19] [paper71]
- On the Anatomy of MCMC-Based Maximum Likelihood Learning of Energy-Based Models [pdf] [code] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Tian Han, Song-Chun Zhu, Ying Nian Wu
2019-04-29, AAAI 2020
- [Energy-Based Models]
[19-12-15] [paper70]
- Implicit Generation and Generalization in Energy-Based Models [pdf] [code] [blog] [pdf with comments] [comments]
- Yilun Du, Igor Mordatch
2019-04-20, NeurIPS 2019
- [Energy-Based Models]
[19-12-14] [paper69]
- Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model [pdf] [poster] [pdf with comments] [comments]
- Erik Nijkamp, Mitch Hill, Song-Chun Zhu, Ying Nian Wu
2019-04-22, NeurIPS 2019
- [Energy-Based Models]
[19-11-29] [paper67]
- Dream to Control: Learning Behaviors by Latent Imagination [pdf] [webpage] [pdf with comments] [comments]
- Anonymous
2019-09
[19-07-11] [paper60]
- Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud [pdf] [pdf with comments] [comments]
- Shaoshuai Shi, Zhe Wang, Xiaogang Wang, Hongsheng Li
2019-07-08
[19-07-03] [paper58]
- Objects as Points [pdf] [code] [pdf with comments] [comments]
- Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
2019-04-16
[19-06-05] [paper55]
- LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving [pdf] [pdf with comments] [comments]
- Gregory P. Meyer, Ankit Laddha, Eric Kee, Carlos Vallespi-Gonzalez, Carl K. Wellington
2019-03-20, CVPR2019
[19-03-01] [paper48]
- Language Models are Unsupervised Multitask Learners [pdf] [blog post] [code] [pdf with comments] [comments]
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever
2019-02-14
[19-02-25] [paper46]
- Evaluating model calibration in classification [pdf] [code] [pdf with comments] [comments]
- Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
2019-02-19, AISTATS2019
[19-02-22] [paper45]
- Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks [pdf] [pdf with comments] [comments]
- Sanjeev Arora, Simon S. Du, Wei Hu, Zhiyuan Li, Ruosong Wang
2019-01-24
[19-02-14] [paper43]
- A Simple Baseline for Bayesian Uncertainty in Deep Learning [pdf] [code] [pdf with comments] [comments]
- Wesley Maddox, Timur Garipov, Pavel Izmailov, Dmitry Vetrov, Andrew Gordon Wilson
2019-02-07
[19-02-13] [paper42]
- Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning [pdf] [code] [pdf with comments] [comments]
- Ruqi Zhang, Chunyuan Li, Jianyi Zhang, Changyou Chen, Andrew Gordon Wilson
2019-02-11
2018:
[20-10-09] [paper107]
- Satellite Conjunction Analysis and the False Confidence Theorem [pdf] [pdf with comments] [comments]
- Michael Scott Balch, Ryan Martin, Scott Ferson
2018-03-21
[19-12-20] [paper73]
- Noise Contrastive Estimation and Negative Sampling for Conditional Models: Consistency and Statistical Efficiency [pdf] [pdf with comments] [comments]
- Zhuang Ma, Michael Collins
2018-09-06, EMNLP 2018
- [Energy-Based Models]
[19-11-26] [paper66]
- Deep Latent Variable Models for Sequential Data [pdf] [pdf with comments] [comments]
- Marco Fraccaro
2018-04-13, PhD Thesis
[19-11-22] [paper65]
- Learning Latent Dynamics for Planning from Pixels [pdf] [code] [blog] [pdf with comments] [comments]
- Danijar Hafner, Timothy Lillicrap, Ian Fischer, Ruben Villegas, David Ha, Honglak Lee, James Davidson
2018-11-12, ICML2019
[19-10-28] [paper64]
- Learning nonlinear state-space models using deep autoencoders [pdf] [pdf with comments] [comments]
- Daniele Masti, Alberto Bemporad
2018, CDC2018
[19-10-04] [paper61]
- Trellis Networks for Sequence Modeling [pdf] [code] [pdf with comments] [comments]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-10-15, ICLR2019
[19-07-10] [paper59]
- PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud [pdf] [code] [pdf with comments] [comments]
- Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
2018-12-11, CVPR2019
[19-06-12] [paper57]
- ATOM: Accurate Tracking by Overlap Maximization [pdf] [code] [pdf with comments] [comments]
- Martin Danelljan, Goutam Bhat, Fahad Shahbaz Khan, Michael Felsberg
2018-11-19, CVPR2019
[19-06-12] [paper56]
- Acquisition of Localization Confidence for Accurate Object Detection [pdf] [code] [oral presentation] [pdf with comments] [comments]
- Borui Jiang, Ruixuan Luo, Jiayuan Mao, Tete Xiao, Yuning Jiang
2018-07-30, ECCV2018
[19-03-29] [paper52]
- Generating High Fidelity Images with Subscale Pixel Networks and Multidimensional Upscaling [pdf] [pdf with comments] [comments]
- Jacob Menick, Nal Kalchbrenner
2018-12-04, ICLR2019
[19-03-04] [paper49]
- Coupled Variational Bayes via Optimization Embedding [pdf] [poster] [code] [pdf with comments] [comments]
- Bo Dai, Hanjun Dai, Niao He, Weiyang Liu, Zhen Liu, Jianshu Chen, Lin Xiao, Le Song
NeurIPS2018
[19-02-27] [paper47]
- Predictive Uncertainty Estimation via Prior Networks [pdf] [pdf with comments] [comments]
- Andrey Malinin, Mark Gales
2018-02-28, NeurIPS2018
[19-02-05] [paper38]
- Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models [pdf] [poster] [video] [code] [pdf with comments] [summary]
- Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine
2018-05-30, NeurIPS2018
[19-01-25] [paper34]
- Meta-Learning For Stochastic Gradient MCMC [pdf] [code] [slides] [pdf with comments] [summary (TODO!)]
- Wenbo Gong, Yingzhen Li, José Miguel Hernández-Lobato
2018-10-28, ICLR2019
[19-01-24] [paper31]
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling [pdf] [code] [pdf with comments] [summary]
- Shaojie Bai, J. Zico Kolter, Vladlen Koltun
2018-04-19
[19-01-17] [paper28]
- How Does Batch Normalization Help Optimization? [pdf] [poster] [video] [pdf with comments] [summary]
- Shibani Santurkar, Dimitris Tsipras, Andrew Ilyas, Aleksander Madry
2018-10-27, NeurIPS2018
[19-01-09] [paper27]
- Relaxed Softmax: Efficient Confidence Auto-Calibration for Safe Pedestrian Detection [pdf] [poster] [pdf with comments] [summary]
- Lukas Neumann, Andrew Zisserman, Andrea Vedaldi
2018-11-29, NeurIPS2018 Workshop
[18-12-12] [paper26]
- Neural Ordinary Differential Equations [pdf] [code] [slides] [pdf with comments] [summary]
- Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, David Duvenaud
2018-10-22, NeurIPS2018
[18-12-06] [paper25]
- Evaluating Bayesian Deep Learning Methods for Semantic Segmentation [pdf] [pdf with comments] [summary]
- Jishnu Mukhoti, Yarin Gal
2018-11-30
[18-11-29] [paper23]
- Evidential Deep Learning to Quantify Classification Uncertainty [pdf] [poster] [code example] [pdf with comments] [summary]
- Murat Sensoy, Lance Kaplan, Melih Kandemir
2018-10-31, NeurIPS2018
[18-11-22] [paper22]
- A Probabilistic U-Net for Segmentation of Ambiguous Images [pdf] [code] [pdf with comments] [summary]
- Simon A. A. Kohl, Bernardino Romera-Paredes, Clemens Meyer, Jeffrey De Fauw, Joseph R. Ledsam, Klaus H. Maier-Hein, S. M. Ali Eslami, Danilo Jimenez Rezende, Olaf Ronneberger
2018-10-29, NeurIPS2018
[18-11-22] [paper21]
- When Recurrent Models Don't Need To Be Recurrent (a.k.a. Stable Recurrent Models) [pdf] [pdf with comments] [summary]
- John Miller, Moritz Hardt
2018-05-29, ICLR2019
[18-11-16] [paper20]
- Uncertainty Estimates and Multi-Hypotheses Networks for Optical Flow [pdf] [pdf with comments] [summary]
- Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, Thomas Brox
2018-08-06, ECCV2018
[18-11-15] [paper19]
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) [pdf] [pdf with comments] [summary]
- Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler, Fernanda Viegas, Rory Sayres
2018-06-07, ICML2018
[18-11-12] [paper18]
- Large-Scale Visual Active Learning with Deep Probabilistic Ensembles [pdf] [pdf with comments] [summary]
- Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
2018-11-08
[18-11-08] [paper17]
- The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks [pdf] [pdf with comments] [summary]
- Jonathan Frankle, Michael Carbin
2018-03-09, ICLR2019
[18-10-26] [paper16]
- Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Klaus Dietmayer
2018-09-08, ITSC2018
[18-10-25] [paper15]
- Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes [pdf] [pdf with comments] [summary]
- Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
2018-10-11, ICLR2019
[18-10-19] [paper14]
- Uncertainty in Neural Networks: Bayesian Ensembling [pdf] [pdf with comments] [summary]
- Tim Pearce, Mohamed Zaki, Alexandra Brintrup, Andy Neel
2018-10-12, AISTATS2019 submission
[18-10-18] [paper12]
- Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors [pdf] [pdf with comments] [summary]
- Danijar Hafner, Dustin Tran, Alex Irpan, Timothy Lillicrap, James Davidson
2018-07-24, ICML2018 Workshop
[18-10-04] [paper10]
- PIXOR: Real-time 3D Object Detection from Point Clouds [pdf] [pdf with comments] [summary]
- Bin Yang, Wenjie Luo, Raquel Urtasun
CVPR2018
[18-10-04] [paper9]
- On gradient regularizers for MMD GANs [pdf] [pdf with comments] [summary]
- Michael Arbel, Dougal J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
2018-05-29, NeurIPS2018
[18-09-30] [paper8]
- Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Jonathan Schwarz, Dan Rosenbaum, Fabio Viola, Danilo J. Rezende, S.M. Ali Eslami, Yee Whye Teh
2018-07-04, ICML2018 Workshop
[18-09-27] [paper7]
- Conditional Neural Processes [pdf] [pdf with comments] [summary]
- Marta Garnelo, Dan Rosenbaum, Chris J. Maddison, Tiago Ramalho, David Saxton, Murray Shanahan, Yee Whye Teh, Danilo J. Rezende, S. M. Ali Eslami
2018-07-04, ICML2018
[18-09-27] [paper6]
- Neural Autoregressive Flows [pdf] [pdf with comments] [summary]
- Chin-Wei Huang, David Krueger, Alexandre Lacoste, Aaron Courville
2018-04-03, ICML2018
[18-09-25] [paper5]
- Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Errors for Deep Neural Networks [pdf] [pdf with comments] [summary]
- Isidro Cortes-Ciriano, Andreas Bender
2018-09-24
[18-09-25] [paper4]
- Leveraging Heteroscedastic Aleatoric Uncertainties for Robust Real-Time LiDAR 3D Object Detection [pdf] [pdf with comments] [summary]
- Di Feng, Lars Rosenbaum, Fabian Timm, Klaus Dietmayer
2018-09-14
[18-09-24] [paper3]
- Lightweight Probabilistic Deep Networks [pdf] [pdf with comments] [summary]
- Jochen Gast, Stefan Roth
2018-05-29, CVPR2018
[18-09-20] [paper1]
- Gaussian Process Behaviour in Wide Deep Neural Networks [pdf] [pdf with comments] [summary]
- Alexander G. de G. Matthews, Mark Rowland, Jiri Hron, Richard E. Turner, Zoubin Ghahramani
2018-08-16, ICLR2018
2017:
[20-02-13] [paper86]
- Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-10-19, ICML 2018
- [Uncertainty Estimation] [Reinforcement Learning]
[20-02-08] [paper85]
- Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables [pdf] [pdf with comments] [comments]
- Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
2017-06-26
- [Uncertainty Estimation] [Reinforcement Learning]
[20-01-10] [paper77]
- Z-Forcing: Training Stochastic Recurrent Networks [pdf] [code] [pdf with comments] [comments]
- Anirudh Goyal, Alessandro Sordoni, Marc-Alexandre Côté, Nan Rosemary Ke, Yoshua Bengio
2017-11-15, NeurIPS 2017
- [VAEs] [Sequence Modeling]
[19-05-29] [paper54]
- Attention Is All You Need [pdf] [pdf with comments] [comments]
- Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin
2017-06-12, NeurIPS2017
[19-04-05] [paper53]
- Stochastic Gradient Descent as Approximate Bayesian Inference [pdf] [pdf with comments] [comments]
- Stephan Mandt, Matthew D. Hoffman, David M. Blei
2017-04-13, Journal of Machine Learning Research 18 (2017)
[19-02-17] [paper44]
- Visualizing the Loss Landscape of Neural Nets [pdf] [code] [pdf with comments] [comments]
- Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, Tom Goldstein
2017-12-28, NeurIPS2018
[19-02-07] [paper40]
- Noisy Natural Gradient as Variational Inference [pdf] [video] [code] [pdf with comments] [comments]
- Guodong Zhang, Shengyang Sun, David Duvenaud, Roger Grosse
2017-12-06, ICML2018
[18-12-05] [paper24]
- On Calibration of Modern Neural Networks [pdf] [code] [pdf with comments] [summary]
- Chuan Guo, Geoff Pleiss, Yu Sun, Kilian Q. Weinberger
2017-08-03, ICML2017
[18-10-18] [paper13]
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles [pdf] [pdf with comments] [summary]
- Balaji Lakshminarayanan, Alexander Pritzel, Charles Blundell
2017-11-17, NeurIPS2017
[18-10-05] [paper11]
- VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection [pdf] [pdf with comments] [summary]
- Yin Zhou, Oncel Tuzel
2017-11-17, CVPR2018
[18-09-24] [paper2]
- What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? [pdf] [pdf with comments] [summary]
- Alex Kendall, Yarin Gal
2017-10-05, NeurIPS2017
2016:
[19-10-18] [paper63]
- Improving Variational Inference with Inverse Autoregressive Flow [pdf] [code] [pdf with comments] [comments]
- Diederik P. Kingma, Tim Salimans, Rafal Jozefowicz, Xi Chen, Ilya Sutskever, Max Welling
2016-06-15, NeurIPS2016
[19-03-15] [paper51]
- A recurrent neural network without chaos [pdf] [pdf with comments] [comments]
- Thomas Laurent, James von Brecht
2016-12-19, ICLR2017
[19-01-26] [paper35]
- Learning Weight Uncertainty with Stochastic Gradient MCMC for Shape Classification [pdf] [poster] [pdf with comments] [comments]
- Chunyuan Li, Andrew Stevens, Changyou Chen, Yunchen Pu, Zhe Gan, Lawrence Carin
CVPR2016
[19-01-24] [paper32]
- Tutorial: Introduction to Stochastic Gradient Markov Chain Monte Carlo Methods [pdf] [pdf with comments]
- Changyou Chen
2016-08-10
2015:
[19-10-11] [paper62]
- Variational Inference with Normalizing Flows [pdf] [pdf with comments] [comments]
- Danilo Jimenez Rezende, Shakir Mohamed
2015-05-21, ICML2015
[19-02-12] [paper41]
- Bayesian Dark Knowledge [pdf] [pdf with comments] [comments]
- Anoop Korattikara, Vivek Rathod, Kevin Murphy, Max Welling
2015-06-07, NeurIPS2015
[19-02-06] [paper39]
- Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks [pdf] [pdf with comments] [comments]
- José Miguel Hernández-Lobato, Ryan P. Adams
2015-07-15, ICML2015
[19-01-27] [paper36]
- Weight Uncertainty in Neural Networks [pdf] [pdf with comments] [comments]
- Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, Daan Wierstra
2015-05-20, ICML2015
[19-01-25] [paper33]
- A Complete Recipe for Stochastic Gradient MCMC [pdf] [pdf with comments] [summary]
- Yi-An Ma, Tianqi Chen, Emily B. Fox
2015-06-15, NeurIPS2015
2014:
[19-03-11] [paper50]
- Auto-Encoding Variational Bayes [pdf] [pdf with comments] [comments]
- Diederik P Kingma, Max Welling
2014-05-01, ICLR2014
[19-01-23] [paper30]
- Stochastic Gradient Hamiltonian Monte Carlo [pdf] [pdf with comments] [summary (TODO!)]
- Tianqi Chen, Emily B. Fox, Carlos Guestrin
2014-05-12, ICML2014
2011:
[19-01-28] [paper37]
- Practical Variational Inference for Neural Networks [pdf] [pdf with comments] [comments]
- Alex Graves
NeurIPS2011
[19-01-23] [paper29]
- Bayesian Learning via Stochastic Gradient Langevin Dynamics [pdf] [pdf with comments] [summary (TODO!)]
- Max Welling, Yee Whye Teh
ICML2011
2010:
[20-01-20] [paper82]
- A Connection Between Score Matching and Denoising Autoencoders [pdf] [pdf with comments] [comments]
- Pascal Vincent
2010-12
- [Energy-Based Models]
2009:
[20-01-14] [paper78]
- Noise-contrastive estimation: A new estimation principle for unnormalized statistical models [pdf] [pdf with comments] [comments]
- Michael Gutmann, Aapo Aapo Hyvärinen
2009, AISTATS 2010
- [Energy-Based Models]
2006:
[19-12-13] [paper68]
- A Tutorial on Energy-Based Learning [pdf] [pdf with comments] [comments]
- Yann LeCun, Sumit Chopra, Raia Hadsell, Marc Aurelio Ranzato, Fu Jie Huang
2006-08-19
- [Energy-Based Models]
2004:
[20-01-16] [paper80]
- Estimation of Non-Normalized Statistical Models by Score Matching [pdf] [pdf with comments] [comments]
- Aapo Hyvärinen
2004-11, JMLR 6
- [Energy-Based Models]
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].