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36 search results found

So Vits Svc ⭐ 20,615

SoftVC VITS Singing Voice Conversion

Deep universal probabilistic programming with Python and PyTorch

Edward ⭐ 4,503

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Gpflow ⭐ 1,802

Gaussian processes in TensorFlow

Bayesian Neural Networks ⭐ 1,633

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

Variational Autoencoder ⭐ 1,129

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

Variational Autoencoder ⭐ 1,049

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

Boltzmann Machines ⭐ 792

Boltzmann Machines in TensorFlow with examples

Awesome Vaes ⭐ 448

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

Bayes Nn ⭐ 423

Lecture notes on Bayesian deep learning

Bayesian Torch ⭐ 396

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

Deep Generative Models For Natural Language Processing ⭐ 330

DGMs for NLP. A roadmap.

Pyvarinf ⭐ 300

Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch

Good Papers ⭐ 231

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

Icml2015_papers ⭐ 143

Links to ICML 2015 papers available on arxiv

Normalizing Flows ⭐ 108

Understanding normalizing flows

Kalman Variational Auto-Encoder

Bayesian dessert for Lasagne

Bayesbyhypernet ⭐ 55

Code for the paper Implicit Weight Uncertainty in Neural Networks

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Bayesian Deep Learning Notes ⭐ 47

A list of notes on Bayesian deep learning papers

Topic Rnn ⭐ 47

Implementation (in progress) of Dieng et al.'s TopicRNN: a neural topic model & RNN hybrid.

Vib Pytorch ⭐ 46

Pytorch implementation of Deep Variational Information Bottleneck

Ai_learning_hub ⭐ 45

AI Learning Hub for Machine Learning, Deep Learning, Computer Vision and Statistics

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

Artificial_neural_networks ⭐ 35

A collection of Methods and Models for various architectures of Artificial Neural Networks

Mixed-curvature Variational Autoencoders (ICLR 2020)

Vae Style Transfer ⭐ 30

An experiment in VAE-based artistic style transfer by embedding fiddling.

Deep Active Inference Mc ⭐ 28

Deep active inference agents using Monte-Carlo methods

Abstractive opinion summarization system (SelSum) and the largest dataset of Amazon product summaries (AmaSum). EMNLP 2021 conference paper.

Deep_variational_information_bottleneck ⭐ 20

Tensorflow implementation of deep variational information bottleneck

Official repository for the paper TRIDENT: Transductive Decoupled Variational Inference for Few Shot Classification

Optimisation Python ⭐ 18

A set of notebooks related to convex optimization, variational inference and numerical methods for signal processing, machine learning, deep learning, graph analysis, bayesian programming, statistics or astronomy.

Haskell Vae ⭐ 17

Learning about Haskell with Variational Autoencoders

Bayesianrnn ⭐ 16

Reproducing the results of the paper "Bayesian Recurrent Neural Networks" by Fortunato et al.

Bayesian Neural Networks Reading List ⭐ 16

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Tfp Tutorial ⭐ 15

TensorFlow Probability Tutorial

Zhusuan Pytorch ⭐ 14

An Elegant Library for Bayesian Deep Learning in PyTorch

Machine Learning Summer Schools ⭐ 14

Curated materials for different machine learning related summer schools

A library designed to implement deep learning algorisms to climate data for weather and climate prediction.

Deep_learning_onboarding ⭐ 8

List of resources for deep learning

Doubly Stochastic Deep Gaussian Process ⭐ 5

Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive uncertainty. Deep Gaussian processes (DGPs) are multi-layer generalisations of GPs, but inference in these models has proved challenging. Existing approaches to inference in DGP models assume approximate posteriors that force independence between the layers, and do not work well in practice. We present a doubly stochastic variational inference

Diverse_sampling ⭐ 5

Official project of DiverseSampling (ACMMM2022 Paper)

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