130 Repositories
Python variational-monte-carlo Libraries
Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax
Clockwork VAEs in JAX/Flax Implementation of experiments in the paper Clockwork Variational Autoencoders (project website) using JAX and Flax, ported
PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
VAENAR-TTS - PyTorch Implementation PyTorch Implementation of VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis.
Implementation for "Manga Filling Style Conversion with Screentone Variational Autoencoder" (SIGGRAPH ASIA 2020 issue)
Manga Filling with ScreenVAE SIGGRAPH ASIA 2020 | Project Website | BibTex This repository is for ScreenVAE introduced in the following paper "Manga F
An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
AlphaZero-Gomoku This is an implementation of the AlphaZero algorithm for playing the simple board game Gomoku (also called Gobang or Five in a Row) f
【CVPR 2021, Variational Inference Framework, PyTorch】 From Rain Generation to Rain Removal
From Rain Generation to Rain Removal (CVPR2021) Hong Wang, Zongsheng Yue, Qi Xie, Qian Zhao, Yefeng Zheng, and Deyu Meng [PDF&&Supplementary Material]
NAS Benchmark in "Prioritized Architecture Sampling with Monto-Carlo Tree Search", CVPR2021
NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper "Prioritized Architecture Sampling with Monto-Carlo Tree
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
VITS: Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech Jaehyeon Kim, Jungil Kong, and Juhee Son In our rece
HNECV: Heterogeneous Network Embedding via Cloud model and Variational inference
HNECV This repository provides a reference implementation of HNECV as described in the paper: HNECV: Heterogeneous Network Embedding via Cloud model a
Clockwork Variational Autoencoder
Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar
This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020
README This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et a
chen2020iros: Learning an Overlap-based Observation Model for 3D LiDAR Localization.
Overlap-based 3D LiDAR Monte Carlo Localization This repo contains the code for our IROS2020 paper: Learning an Overlap-based Observation Model for 3D
Decipher using Markov Chain Monte Carlo
Decipher using Markov Chain Monte Carlo
Collaborative variational bandwidth auto-encoder (VBAE) for recommender systems.
Collaborative Variational Bandwidth Auto-encoder The codes are associated with the following paper: Collaborative Variational Bandwidth Auto-encoder f
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an
[CVPR 2021 Oral] Variational Relational Point Completion Network
VRCNet: Variational Relational Point Completion Network This repository contains the PyTorch implementation of the paper: Variational Relational Point
DVG-Face: Dual Variational Generation for Heterogeneous Face Recognition, TPAMI 2021
DVG-Face: Dual Variational Generation for HFR This repo is a PyTorch implementation of DVG-Face: Dual Variational Generation for Heterogeneous Face Re
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks
Bayesian-Torch is a library of neural network layers and utilities extending the core of PyTorch to enable the user to perform stochastic variational inference in Bayesian deep neural networks. Bayesian-Torch is designed to be flexible and seamless in extending a deterministic deep neural network architecture to corresponding Bayesian form by simply replacing the deterministic layers with Bayesian layers.
Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms
AdvancedHMC.jl AdvancedHMC.jl provides a robust, modular and efficient implementation of advanced HMC algorithms. An illustrative example for Advanced
Official implementation for Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020
Likelihood-Regret Official implementation of Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder at NeurIPS 2020. T
A probabilistic programming language in TensorFlow. Deep generative models, variational inference.
Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilis
Gaussian processes in TensorFlow
Website | Documentation (release) | Documentation (develop) | Glossary Table of Contents What does GPflow do? Installation Getting Started with GPflow
Deep universal probabilistic programming with Python and PyTorch
Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab
Diverse Image Captioning with Context-Object Split Latent Spaces (NeurIPS 2020)
Diverse Image Captioning with Context-Object Split Latent Spaces This repository is the PyTorch implementation of the paper: Diverse Image Captioning
Modular Probabilistic Programming on MXNet
MXFusion | | | | Tutorials | Documentation | Contribution Guide MXFusion is a modular deep probabilistic programming library. With MXFusion Modules yo
Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
PyVarInf PyVarInf provides facilities to easily train your PyTorch neural network models using variational inference. Bayesian Deep Learning with Vari
Bayesian dessert for Lasagne
Gelato Bayesian dessert for Lasagne Recent results in Bayesian statistics for constructing robust neural networks have proved that it is one of the be
Deep universal probabilistic programming with Python and PyTorch
Getting Started | Documentation | Community | Contributing Pyro is a flexible, scalable deep probabilistic programming library built on PyTorch. Notab
Open-AI's DALL-E for large scale training in mesh-tensorflow.
DALL-E in Mesh-Tensorflow [WIP] Open-AI's DALL-E in Mesh-Tensorflow. If this is similarly efficient to GPT-Neo, this repo should be able to train mode
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS)
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech (BVAE-TTS) Yoonhyung Lee, Joongbo Shin, Kyomin Jung Abstract: Although early
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano
PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an