prosperA Python Library for Probabilistic Sparse Coding with Non-Standard Priors and Superpositions
Stars: ✭ 17 (-88.36%)
PyLDAA Latent Dirichlet Allocation implementation in Python.
Stars: ✭ 51 (-65.07%)
Probabilistic unetA U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
Stars: ✭ 427 (+192.47%)
normalizing-flowsPyTorch implementation of normalizing flow models
Stars: ✭ 271 (+85.62%)
Pytorch BayesiancnnBayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
Stars: ✭ 779 (+433.56%)
VINFRepository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen
Stars: ✭ 23 (-84.25%)
Inverse rlAdversarial Imitation Via Variational Inverse Reinforcement Learning
Stars: ✭ 79 (-45.89%)
AI Learning HubAI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics
Stars: ✭ 53 (-63.7%)
autoreparamAutomatic Reparameterisation of Probabilistic Programs
Stars: ✭ 29 (-80.14%)
Bayesian Neural NetworksPytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Stars: ✭ 900 (+516.44%)
GpflowGaussian processes in TensorFlow
Stars: ✭ 1,547 (+959.59%)
probai-2021-pyroRepo for the Tutorials of Day1-Day3 of the Nordic Probabilistic AI School 2021 (https://probabilistic.ai/)
Stars: ✭ 45 (-69.18%)
Pymc3Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
Stars: ✭ 6,214 (+4156.16%)
ReactiveMP.jlJulia package for automatic Bayesian inference on a factor graph with reactive message passing
Stars: ✭ 58 (-60.27%)
Bayes By BackpropPyTorch implementation of "Weight Uncertainty in Neural Networks"
Stars: ✭ 119 (-18.49%)
SelSumAbstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.
Stars: ✭ 36 (-75.34%)
ProbregPython package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
Stars: ✭ 306 (+109.59%)
ccubeBayesian mixture models for estimating and clustering cancer cell fractions
Stars: ✭ 23 (-84.25%)
Rnn VaeVariational Autoencoder with Recurrent Neural Network based on Google DeepMind's "DRAW: A Recurrent Neural Network For Image Generation"
Stars: ✭ 39 (-73.29%)
boundary-gpKnow Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
Stars: ✭ 21 (-85.62%)
cmdstanrCmdStanR: the R interface to CmdStan
Stars: ✭ 82 (-43.84%)
Probabilistic ModelsCollection of probabilistic models and inference algorithms
Stars: ✭ 217 (+48.63%)
sqairImplementation of Sequential Attend, Infer, Repeat (SQAIR)
Stars: ✭ 96 (-34.25%)
GpstuffGPstuff - Gaussian process models for Bayesian analysis
Stars: ✭ 106 (-27.4%)
DropoutsPyTorch Implementations of Dropout Variants
Stars: ✭ 72 (-50.68%)
Variational AutoencoderVariational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
Stars: ✭ 807 (+452.74%)
haskell-vaeLearning about Haskell with Variational Autoencoders
Stars: ✭ 18 (-87.67%)
VbmcVariational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
Stars: ✭ 123 (-15.75%)
noisy-K-FACNatural Gradient, Variational Inference
Stars: ✭ 29 (-80.14%)
artificial neural networksA collection of Methods and Models for various architectures of Artificial Neural Networks
Stars: ✭ 40 (-72.6%)
MxfusionModular Probabilistic Programming on MXNet
Stars: ✭ 95 (-34.93%)
BayesByHypernetCode for the paper Implicit Weight Uncertainty in Neural Networks
Stars: ✭ 63 (-56.85%)
Bayes NnLecture notes on Bayesian deep learning
Stars: ✭ 444 (+204.11%)
active-inferenceA toy model of Friston's active inference in Tensorflow
Stars: ✭ 36 (-75.34%)
Tensorflow Mnist CvaeTensorflow implementation of conditional variational auto-encoder for MNIST
Stars: ✭ 139 (-4.79%)
adaptive-f-divergenceA tensorflow implementation of the NIPS 2018 paper "Variational Inference with Tail-adaptive f-Divergence"
Stars: ✭ 20 (-86.3%)
Awesome VaesA curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
Stars: ✭ 418 (+186.3%)
rssRegression with Summary Statistics.
Stars: ✭ 42 (-71.23%)
Deepbayes 2018Seminars DeepBayes Summer School 2018
Stars: ✭ 1,021 (+599.32%)
DUNCode for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Stars: ✭ 65 (-55.48%)
Generative models tutorial with demoGenerative Models Tutorial with Demo: Bayesian Classifier Sampling, Variational Auto Encoder (VAE), Generative Adversial Networks (GANs), Popular GANs Architectures, Auto-Regressive Models, Important Generative Model Papers, Courses, etc..
Stars: ✭ 276 (+89.04%)
vireoDemultiplexing pooled scRNA-seq data with or without genotype reference
Stars: ✭ 34 (-76.71%)
BcpdBayesian Coherent Point Drift (BCPD/BCPD++); Source Code Available
Stars: ✭ 116 (-20.55%)
SIVIUsing neural network to build expressive hierarchical distribution; A variational method to accurately estimate posterior uncertainty; A fast and general method for Bayesian inference. (ICML 2018)
Stars: ✭ 49 (-66.44%)
viabelEfficient, lightweight variational inference and approximation bounds
Stars: ✭ 27 (-81.51%)
Good PapersI try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
Stars: ✭ 248 (+69.86%)
Gp Infer NetScalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
Stars: ✭ 37 (-74.66%)
lagvaeLagrangian VAE
Stars: ✭ 27 (-81.51%)
Celeste.jlScalable inference for a generative model of astronomical images
Stars: ✭ 142 (-2.74%)
KvaeKalman Variational Auto-Encoder
Stars: ✭ 115 (-21.23%)
PyroDeep universal probabilistic programming with Python and PyTorch
Stars: ✭ 7,224 (+4847.95%)
CIKM18-LCVACode for CIKM'18 paper, Linked Causal Variational Autoencoder for Inferring Paired Spillover Effects.
Stars: ✭ 13 (-91.1%)