Alternatives and similar repositories for bnn

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

• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 4 years ago
• facebookresearch / SingleModelUncertainty
  Learning error bars for neural network predictions
  ☆70Updated 5 years ago
• nitarshan / bayes-by-backprop
  PyTorch implementation of "Weight Uncertainty in Neural Networks"
  ☆176Updated 3 years ago
• automl / pybnn
  Bayesian neural network package
  ☆147Updated 3 years ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆100Updated 6 years ago
• EmilienDupont / neural-processes
  Pytorch implementation of Neural Processes for functions and images
  ☆232Updated 3 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆82Updated last year
• wassname / attentive-neural-processes
  implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
  ☆95Updated 3 months ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆85Updated 4 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆93Updated 7 years ago
• cics-nd / gptorch
  Gaussian processes with PyTorch
  ☆30Updated 3 years ago
• hughsalimbeni / bayesian_benchmarks
  A community repository for benchmarking Bayesian methods
  ☆110Updated 3 years ago
• ThirstyScholar / bayes-by-backprop
  " Weight Uncertainty in Neural Networks"
  ☆49Updated 7 years ago
• Kyushik / Predictive-Uncertainty-Estimation-using-Deep-Ensemble
  This repository is the code for Predictive Uncertainty Estimation using Deep Ensemble
  ☆157Updated 2 years ago
• yaringal / HeteroscedasticDropoutUncertainty
  Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.
  ☆140Updated 9 years ago
• UCL-SML / Doubly-Stochastic-DGP
  Deep Gaussian Processes with Doubly Stochastic Variational Inference
  ☆150Updated 6 years ago
• brain-research / nngp
  Deep neural network kernel for Gaussian process
  ☆207Updated 4 years ago
• sksq96 / pytorch-mdn
  Mixture Density Networks (MDN) implemented in PyTorch
  ☆65Updated 5 years ago
• vvanirudh / deep-ensembles-uncertainty
  My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"
  ☆138Updated 7 years ago
• ssydasheng / Bayesian_neural_network_papers
  Papers for Bayesian-NN
  ☆325Updated 6 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• cpark321 / uncertainty-deep-learning
  ☆238Updated 5 years ago
• SheffieldML / PyDeepGP
  Deep Gaussian Processes in Python
  ☆234Updated 4 years ago
• kumar-shridhar / Master-Thesis-BayesianCNN
  Master Thesis on Bayesian Convolutional Neural Network using Variational Inference
  ☆264Updated 5 years ago
• simonkamronn / kvae
  Kalman Variational Auto-Encoder
  ☆136Updated 6 years ago
• guxd / deepHMM
  A PyTorch implementation of a Deep Hidden Markov Model [Structured Inference Networks for Nonlinear State Space Models]
  ☆57Updated 11 months ago
• boschresearch / PR-SSM
  Python implementation of the PR-SSM.
  ☆51Updated 6 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 6 years ago
• pmorenoz / HetMOGP
  Heterogeneous Multi-output Gaussian Processes
  ☆52Updated 5 years ago
• YannDubs / Neural-Process-Family
  Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
  ☆223Updated last year