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variational-inference

Here are 364 public repositories matching this topic...

pyro

pyro-ppl / pyro

Deep universal probabilistic programming with Python and PyTorch

python machine-learning deep-learning pytorch probabilistic-programming bayesian bayesian-inference variational-inference probabilistic-modeling

Updated Mar 16, 2023
Python

pymc-devs / pymc

Bayesian Modeling in Python

python statistical-analysis probabilistic-programming bayesian-inference mcmc variational-inference pytensor

Updated Mar 16, 2023
Python

GPflow / GPflow

Gaussian processes in TensorFlow

machine-learning deep-learning tensorflow ml gaussian-processes variational-inference bayesian-statistics markov-chain-monte-carlo stochastic-processes gp gpflow

Updated Mar 16, 2023
Python

JavierAntoran / Bayesian-Neural-Networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

deep-learning reproducible-research regression pytorch uncertainty classification uncertainty-neural-networks bayesian-inference mcmc variational-inference hmc bayesian-neural-networks langevin-dynamics approximate-inference local-reparametrization-trick kronecker-factored-approximation mc-dropout bayes-by-backprop out-of-distribution-detection sgld

Updated May 1, 2020
Jupyter Notebook

kumar-shridhar / PyTorch-BayesianCNN

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

python pytorch bayesian-network image-recognition convolutional-neural-networks bayesian-inference bayes bayesian-networks variational-inference bayesian-statistics bayesian-neural-networks variational-bayes bayesian-deep-learning pytorch-cnn bayesian-convnets bayes-by-backprop aleatoric-uncertainties

Updated Feb 5, 2021
Python

altosaar / variational-autoencoder

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

learning machine-learning deep-neural-networks deep-learning tensorflow deep pytorch vae unsupervised-learning variational-inference probabilistic-graphical-models variational-autoencoder autoregressive-neural-networks

Updated Nov 11, 2021
Python

bayesgroup / deepbayes-2018

Seminars DeepBayes Summer School 2018

deep-learning bayesian variational-inference

Updated Aug 24, 2019
Jupyter Notebook

janosh / awesome-normalizing-flows

Sponsor

Awesome resources on normalizing flows.

machine-learning awesome-list bayesian-inference autoregressive variational-inference density-estimation normalizing-flows bayesian-neural-networks generative-modeling

Updated Feb 27, 2023
Python

yell / boltzmann-machines

Boltzmann Machines in TensorFlow with examples

machine-learning deep-learning tensorflow keras restricted-boltzmann-machine rbm dbm boltzmann-machines mcmc variational-inference gibbs-sampling ais sklearn-compatible tensorflow-models pcd contrastive-divergence-algorithm energy-based-model annealed-importance-sampling

Updated Nov 5, 2021
Jupyter Notebook

matthewvowels1 / Awesome-VAEs

A curated list of awesome work on VAEs, disentanglement, representation learning, and generative models.

deep-learning generative-model vae awesome-list representation-learning unsupervised-learning variational-inference variational-autoencoder variational-bayes disentanglement variational-auto-encoder disentangled-representations

Updated Jul 11, 2021

neka-nat / probreg

Sponsor

Python package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)

point-cloud registration gaussian-mixture-models expectation-maximization-algorithm variational-inference 3d dual-quaternion point-cloud-registration open3d coherent-point-drift non-rigid-registration rigid-transformations filterreg dual-quaternion-skinning

Updated Sep 12, 2022
Python

SimonKohl / probabilistic_unet

A U-Net combined with a variational auto-encoder that is able to learn conditional distributions over semantic segmentations.

variational-inference semantic-segmentation nips generative-models variational-autoencoders u-net nips-2018 neurips neurips-2018

Updated Nov 22, 2022
Jupyter Notebook

sjchoi86 / bayes-nn

Lecture notes on Bayesian deep learning

deep-learning variational-inference probability-theory

Updated Feb 11, 2018

fehiepsi / rethinking-numpyro

Statistical Rethinking (2nd ed.) with NumPyro

python numpy variational-inference causal-inference bayesian-statistics markov-chain-monte-carlo laplace-approximation

Updated Jan 31, 2023
Jupyter Notebook

FranxYao / Deep-Generative-Models-for-Natural-Language-Processing

DGMs for NLP. A roadmap.

natural-language-processing parsing decoding text-generation generative-text generative-model graphical-models mcmc language-model generative-adversarial-networks variational-inference markov-chain-monte-carlo latent-variable-models normalizing-flows structured-prediction discrete-structures generative-models variational-autoencoders approximate-inference gradient-estimation

Updated Dec 12, 2022

VincentStimper / normalizing-flows

PyTorch implementation of normalizing flow models

pytorch variational-inference density-estimation invertible-neural-networks variational-autoencoder glow normalizing-flow real-nvp residual-flow neural-spline-flow

Updated Feb 25, 2023
Python

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..

tutorial generative-adversarial-network dcgan generative-model gaussian-mixture-models auto-regressive-model gans bayesian-classifiers variational-inference tutorial-code variational-autoencoder cyclegan

Updated Jan 21, 2019
Jupyter Notebook

bahjat-kawar / ddrm

[NeurIPS 2022] Denoising Diffusion Restoration Models -- Official Code Repository

super-resolution inverse-problems variational-inference diffusion inpainting deblurring diffusion-models score-based

Updated Oct 10, 2022
Python

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

machine-learning natural-language-processing computer-vision deep-learning neural-network semi-supervised-learning convolutional-networks neural-machine-translation representation-learning gaussian-processes variational-inference kernel-learning variational-autoencoders deep-kernel-learning

Updated Jun 5, 2019

ksachdeva / rethinking-tensorflow-probability

Statistical Rethinking (2nd Ed) with Tensorflow Probability

tensorflow tutorials bayesian-inference variational-inference bayesian-statistics markov-chain-monte-carlo tensorflow-probability statistical-rethinking

Updated Jan 19, 2022
Jupyter Notebook

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