team-approx-bayes / dl-with-bayes

Related projects: ⓘ

• izmailovpavel / understandingbdl
  ☆231Updated last year
• SamsungLabs / pytorch-ensembles
  Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020
  ☆235Updated last year
• p-lambda / verified_calibration
  Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlig…
  ☆141Updated last year
• wjmaddox / swa_gaussian
  Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"
  ☆438Updated last year
• RobRomijnders / weight_uncertainty
  Implementing Bayes by Backprop
  ☆181Updated 5 years ago
• google / uncertainty-metrics
  ☆146Updated this week
• emtiyaz / vadam
  Code for ICML 2018 paper on "Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam" by Khan, Nielsen, Tangkaratt, Lin, …
  ☆109Updated 5 years ago
• nitarshan / bayes-by-backprop
  PyTorch implementation of "Weight Uncertainty in Neural Networks"
  ☆170Updated 2 years ago
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆267Updated 2 years ago
• ctallec / pyvarinf
  Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch
  ☆358Updated 4 years ago
• ssydasheng / Bayesian_neural_network_papers
  Papers for Bayesian-NN
  ☆314Updated 5 years ago
• ruqizhang / csgmcmc
  Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
  ☆91Updated 3 years ago
• cybertronai / pytorch-sso
  PyTorch-SSO: Scalable Second-Order methods in PyTorch
  ☆141Updated 11 months ago
• microsoft / deterministic-variational-inference
  Sample code for running deterministic variational inference to train Bayesian neural networks
  ☆99Updated 5 years ago
• yaringal / ConcreteDropout
  Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832
  ☆246Updated 5 years ago
• OATML / bdl-benchmarks
  Bayesian Deep Learning Benchmarks
  ☆661Updated last year
• otokonoko8 / implicit-variational-inference
  The collection of recent papers about variational inference
  ☆84Updated 4 years ago
• ex4sperans / variational-inference-with-normalizing-flows
  Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)
  ☆224Updated 6 years ago
• BayesWatch / deep-kernel-transfer
  Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
  ☆196Updated 2 years ago
• wgrathwohl / JEM
  Project site for "Your Classifier is Secretly an Energy-Based Model and You Should Treat it Like One"
  ☆415Updated 2 years ago
• gbaydin / hypergradient-descent
  Hypergradient descent
  ☆137Updated 3 months ago
• EmilienDupont / neural-processes
  Pytorch implementation of Neural Processes for functions and images
  ☆223Updated 2 years ago
• josipd / torch-two-sample
  A PyTorch library for two-sample tests
  ☆236Updated last year
• abdulfatir / normalizing-flows
  Understanding normalizing flows
  ☆131Updated 4 years ago
• google-research / robustness_metrics
  ☆462Updated last month
• riannevdberg / sylvester-flows
  ☆177Updated 5 years ago
• microsoft / bayesianize
  Bayesianize: A Bayesian neural network wrapper in pytorch
  ☆81Updated 4 months ago
• hutec / UncertaintyNN
  Implementation and evaluation of different approaches to get uncertainty in neural networks
  ☆139Updated 6 years ago
• LeoYu / neural-tangent-kernel-UCI
  Testing Nerual Tangent Kernel (NTK) on small UCI datasets
  ☆82Updated 4 years ago
• suinleelab / attributionpriors
  Tools for training explainable models using attribution priors.
  ☆121Updated 3 years ago