vlgiitr / Papers_we_read
Summaries of the papers that are discussed by VLG.
Stars: β 203
Projects that are alternatives of or similar to Papers we read
Reinforcement Learning
π€ Implements of Reinforcement Learning algorithms.
Stars: β 104 (-48.77%)
Mutual labels: papers, reinforcement-learning
Drl papernotes
Notes and comments about Deep Reinforcement Learning papers
Stars: β 65 (-67.98%)
Mutual labels: papers, reinforcement-learning
Conversational Ai
Conversational AI Reading Materials
Stars: β 34 (-83.25%)
Mutual labels: papers, reinforcement-learning
Sa Papers
π Deep Learning δΈ Sentiment Analysis θ«ζη΅±ζ΄θεζ ππ‘βΉοΈπππ€’
Stars: β 111 (-45.32%)
Mutual labels: review, papers
Neural Logic Machines
Implementation for the Neural Logic Machines (NLM).
Stars: β 197 (-2.96%)
Mutual labels: reinforcement-learning
Free Ai Resources
π FREE AI Resources - π Courses, π· Jobs, π Blogs, π¬ AI Research, and many more - for everyone!
Stars: β 192 (-5.42%)
Mutual labels: reinforcement-learning
Gym Sokoban
Sokoban environment for OpenAI Gym
Stars: β 186 (-8.37%)
Mutual labels: reinforcement-learning
Sysml Reading List
Systems for ML/AI & ML/AI for Systems paper reading list: A curated reading list of computer science research for work at the intersection of machine learning and systems. PR are welcome.
Stars: β 202 (-0.49%)
Mutual labels: papers
Knowledge graph reasoning papers
Must-read papers on knowledge graph reasoning
Stars: β 201 (-0.99%)
Mutual labels: reinforcement-learning
Paac
Open source implementation of the PAAC algorithm presented in Efficient Parallel Methods for Deep Reinforcement Learning
Stars: β 196 (-3.45%)
Mutual labels: reinforcement-learning
Naf Tensorflow
"Continuous Deep Q-Learning with Model-based Acceleration" in TensorFlow
Stars: β 192 (-5.42%)
Mutual labels: reinforcement-learning
My bibliography for research on autonomous driving
Personal notes about scientific and research works on "Decision-Making for Autonomous Driving"
Stars: β 197 (-2.96%)
Mutual labels: reinforcement-learning
Sputnik
Static code review for your Gerrit patchsets. Runs Checkstyle, PMD, FindBugs, Scalastyle, CodeNarc, JSLint for you!
Stars: β 189 (-6.9%)
Mutual labels: review
Release
Deep Reinforcement Learning for de-novo Drug Design
Stars: β 201 (-0.99%)
Mutual labels: reinforcement-learning
Kbgan
Code for "KBGAN: Adversarial Learning for Knowledge Graph Embeddings" https://arxiv.org/abs/1711.04071
Stars: β 186 (-8.37%)
Mutual labels: reinforcement-learning
Drl4recsys
Courses on Deep Reinforcement Learning (DRL) and DRL papers for recommender systems
Stars: β 196 (-3.45%)
Mutual labels: reinforcement-learning
Dm control
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.
Stars: β 2,592 (+1176.85%)
Mutual labels: reinforcement-learning
Reinforcementlearning.jl
A reinforcement learning package for Julia
Stars: β 192 (-5.42%)
Mutual labels: reinforcement-learning
Deep Learning Paper Summaries
Summaries for papers discussed by VLG.
Summaries
2020
- Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild [Paper][Review]
- Shangzhe Wu, Christian Rupprecht, Andrea Vedaldi, CVPR 2020
- You Only Train Once: Loss-conditional training of deep networks [Paper][Review]
- Alexey Dosovitskiy, Josip Djolonga, ICLR 2020
- GrokNet: Unified Computer Vision Model Trunk and Embeddings For Commerce [Paper][Review]
- Sean Bell, Yiqun Liu, Sami Alsheikh, Yina Tang, Ed Pizzi, M. Henning, Karun Singh, Omkar Parkhi, Fedor Borisyuk, KDD 2020
- Semantically multi-modal image synthesis [Paper][Review]
- Zhen Zhu, Zhiliang Xu, Ansheng You, Xiang Bai, CVPR 2020
- Learning to Simulate Dynamic Environments with GameGAN [Paper][Review]
- Seung Wook Kim, Yuhao Zhou, Jonah Philion, Antonio Torralba, Sanja Fidler, CVPR 2020
- Adversarial Policies : Attacking deep reinforcement learning [Paper][Review]
- Adam Gleave, Michael Dennis, Cody Wild, Neel Kant, Sergey Levine, Stuart Russell, ICLR 2020
- Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning [Paper][Review]
- Jean-Bastien Grill, Florian Strub, Florent AltchΓ©, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, RΓ©mi Munos, Michal Valko, CVPR 2020
2019
- ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks [Paper][Review]
- Jiasen Lu, Dhruv Batra, Devi Parikh, Stefan Lee, NIPS 2019
- Stand-Alone Self-Attention in Vision Models [Paper][Review]
- Prajit Ramachandran, Niki Parmar, Ashish Vaswani, Irwan Bello, Anselm Levskaya, Jonathon Shlens, NIPS 2019
- Zero-Shot Entity Linking by Reading Entity Descriptions [Paper][Review]
- Lajanugen Logeswaran , Ming-Wei Changβ‘ Kenton Lee , Kristina Toutanova , Jacob Devlin, Honglak Lee ACL-2019
- Do you know that Florence is packed with visitors? Evaluating state-of-the-art models of speaker commitment [Paper][Review]
- Nanjiang Jiang and Marie-Catherine de Marneffe , ACL-2019
- Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations [Paper][Review]
- Vincent Sitzmann, Michael Zollhofer, Gordon Wetzstein, NIPS-2019
- Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts [Paper][Review]
- Rui Xia, Zixiang Ding, ACL-2019
- Putting an End to End-to-End: Gradient-Isolated Learning of Representations [Paper][Review]
- Sindy Lowe, Peter O' Connor, Bastiaan S. Veeling, NIPS-2019
- Bridging the Gap between Training and Inference for Neural Machine Translation [Paper][Review]
- Wen Zhang, Yang Feng, Fandong Meng, Di You, Qun Liu, ACL-2019
- Designing and Interpreting Probes with Control Tasks [Paper][Review]
- John Hewitt, Percy Liang, EMNLP-2019
- Specializing Word Embeddings (for Parsing) by Information Bottleneck [Paper][Review]
- Xiang Lisa Li, Jason Eisner, EMNLP-2019
- vGraph: A Generative Model for Joint Community Detection and Node Representational Learning [Paper] [Review]
- Fan-Yun Sun, Meng Qu, Jordan Hoffmann, Chin-Wei Huang, Jian Tang, NIPS-2019
- Uniform convergence may be unable to explain generalization in deep learning [Paper][Review]
- Vaishnavh Nagarajan, J. Zico Kolter, NIPS-2019
- SinGAN: Learning a Generative Model from a Single Natural Image [Paper] [Review]
- Tamar Rott Shaham, Tali Dekel, Tomer Michaeli, ICCV-2019
- Graph U-Nets [Paper] [Review]
- Hongyang Gao, Shuiwang Ji, ICML-2019
- Feature Denoising for Improving Adversarial Robustness [Paper] [Review]
- Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan Yuille, kaiming He, CVPR-2019
- This Looks Like That: Deep Learning for Interpretable Image Recognition [Paper] [Review]
- Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, Cynthia Rudin, NIPS-2019
2018
- CyCADA: Cycle-Consistent Adversarial Domain Adaptation [Paper] [Review]
- Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell, ICML-2018
2017
- Unpaired Image-to-Image Translation using Cycle Consistent Adversarial Networks [Paper] [Review]
- Jun-Yan Zhu, Taesung Park, Phillip Isola, Alexei A. Efros, ICCV-2017
- Densely Connected Convolutional Networks [Paper] [Review]
- Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger, CVPR-2017
2016
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].