variational-inference
There are 443 repositories under variational-inference topic.
svc-develop-team/so-vits-svc
SoftVC VITS Singing Voice Conversion
pymc-devs/pymc
Bayesian Modeling and Probabilistic Programming in Python
pyro-ppl/pyro
Deep universal probabilistic programming with Python and PyTorch
JavierAntoran/Bayesian-Neural-Networks
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
GPflow/GPflow
Gaussian processes in TensorFlow
janosh/awesome-normalizing-flows
Awesome resources on normalizing flows.
kumar-shridhar/PyTorch-BayesianCNN
Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.
jaanli/variational-autoencoder
Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)
bayesgroup/deepbayes-2018
Seminars DeepBayes Summer School 2018
neka-nat/probreg
Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
VincentStimper/normalizing-flows
PyTorch implementation of normalizing flow models
yell/boltzmann-machines
Boltzmann Machines in TensorFlow with examples
matthewvowels1/Awesome-VAEs
A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.
bahjat-kawar/ddrm
[NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository
SimonKohl/probabilistic_unet
A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.
sjchoi86/bayes-nn
Lecture notes on Bayesian deep learning
fehiepsi/rethinking-numpyro
Statistical Rethinking (2nd ed.) with NumPyro
FranxYao/Deep-Generative-Models-for-Natural-Language-Processing
DGMs for NLP. A roadmap.
ReactiveBayes/RxInfer.jl
Julia package for automated Bayesian inference on a factor graph with reactive message passing
omerbsezer/Generative_Models_Tutorial_with_Demo
Generative 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..
ohirose/bcpd
Bayesian Coherent Point Drift / Domain Elastic Transform
hoangcuong2011/Good-Papers
I 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
ksachdeva/rethinking-tensorflow-probability
Statistical Rethinking (2nd Ed) with Tensorflow Probability
wiseodd/probabilistic-models
Collection of probabilistic models and inference algorithms
ex4sperans/variational-inference-with-normalizing-flows
Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
acerbilab/vbmc
Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference in MATLAB
ermongroup/Variational-Ladder-Autoencoder
Implementation of VLAE
1Konny/VIB-pytorch
Pytorch implementation of Deep Variational Information Bottleneck
jeff-regier/Celeste.jl
Scalable inference for a generative model of astronomical images
nitarshan/bayes-by-backprop
PyTorch implementation of "Weight Uncertainty in Neural Networks"
gpstuff-dev/gpstuff
GPstuff - Gaussian process models for Bayesian analysis
stan-dev/cmdstanr
CmdStanR: the R interface to CmdStan
hwalsuklee/tensorflow-mnist-CVAE
Tensorflow implementation of conditional variational auto-encoder for MNIST
simonkamronn/kvae
Kalman Variational Auto-Encoder
abdulfatir/normalizing-flows
Understanding normalizing flows
acerbilab/pyvbmc
PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python