Alternatives and similar repositories for vadam

Users that are interested in vadam are comparing it to the libraries listed below

• brain-research / ncp
  Reliable Uncertainty Estimates in Deep Neural Networks using Noise Contrastive Priors
  ☆62Updated 5 years ago
• AMLab-Amsterdam / MNF_VBNN
  Multiplicative Normalizing Flow (MNF) posteriors for variational Bayesian neural networks
  ☆66Updated 5 years ago
• pawni / BayesByHypernet
  Code for the paper Implicit Weight Uncertainty in Neural Networks
  ☆65Updated 5 years ago
• bneyshabur / over-parametrization
  Computing various norms/measures on over-parametrized neural networks
  ☆50Updated 6 years ago
• gbaydin / hypergradient-descent
  Hypergradient descent
  ☆148Updated last year
• asteroidhouse / self-tuning-networks
  Code for Self-Tuning Networks (ICLR 2019) https://arxiv.org/abs/1903.03088
  ☆54Updated 6 years ago
• kefirski / variational_dropout
  Implementation of "Variational Dropout and the Local Reparameterization Trick" paper with Pytorch
  ☆48Updated 7 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 7 years ago
• pomonam / NoisyNaturalGradient
  TensorFlow implementation of "noisy K-FAC" and "noisy EK-FAC".
  ☆60Updated 6 years ago
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆132Updated 5 years ago
• skolouri / swae
  Implementation of the Sliced Wasserstein Autoencoders
  ☆90Updated 7 years ago
• SebFar / radial_bnn
  Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
  ☆33Updated 5 years ago
• joelouismarino / iterative_inference
  Implementation of iterative inference in deep latent variable models
  ☆43Updated 6 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆85Updated 5 years ago
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆42Updated 5 years ago
• emtiyaz / vmp-for-svae
  Variational Message Passing for Structured VAE (Code for ICLR 2018 paper)
  ☆45Updated 7 years ago
• ssydasheng / Neural-Kernel-Network
  Code for "Differentiable Compositional Kernel Learning for Gaussian Processes" https://arxiv.org/abs/1806.04326
  ☆71Updated 7 years ago
• wlwkgus / NoisyNaturalGradient
  Pytorch Implementation of paper "Noisy Natural Gradient as Variational Inference"
  ☆121Updated 7 years ago
• ucla-vision / information-dropout
  Implementation of Information Dropout
  ☆39Updated 8 years ago
• da-molchanov / variance-networks
  Variance Networks: When Expectation Does Not Meet Your Expectations, ICLR 2019
  ☆39Updated 5 years ago
• j-min / Dropouts
  PyTorch Implementations of Dropout Variants
  ☆87Updated 7 years ago
• thjashin / gp-infer-net
  Scalable Training of Inference Networks for Gaussian-Process Models, ICML 2019
  ☆41Updated 2 years ago
• lrjconan / RBP
  Recurrent Back Propagation, Back Propagation Through Optimization, ICML 2018
  ☆43Updated 6 years ago
• ruqizhang / csgmcmc
  Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
  ☆92Updated 4 years ago
• wittawatj / kernel-gof
  NeurIPS 2017 best paper. An interpretable linear-time kernel goodness-of-fit test.
  ☆67Updated 6 years ago
• rhaps0dy / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆39Updated 6 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 7 years ago
• uber-research / loss-change-allocation
  ☆61Updated 2 years ago
• toiaydcdyywlhzvlob / backpack
  This repository is no longer maintained. Check
  ☆81Updated 5 years ago
• IraKorshunova / bruno
  a deep recurrent model for exchangeable data
  ☆34Updated 5 years ago