izmailovpavel / bnn_covariate_shift

Related projects ⓘ

Alternatives and complementary repositories for bnn_covariate_shift

• team-approx-bayes / fromp
  Contains code for the NeurIPS 2020 paper by Pan et al., "Continual Deep Learning by FunctionalRegularisation of Memorable Past"
  ☆44Updated 3 years ago
• MichaelArbel / GeneralizedEBM
  ☆52Updated 3 months ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆72Updated last year
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 4 years ago
• microsoft / bayesianize
  Bayesianize: A Bayesian neural network wrapper in pytorch
  ☆86Updated 5 months ago
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆35Updated 2 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆86Updated 4 years ago
• MJ10 / DEUP
  Code for experiments to learn uncertainty
  ☆30Updated last year
• AMLab-Amsterdam / FNP
  Implementation of the Functional Neural Process models
  ☆43Updated 4 years ago
• fbickfordsmith / epig
  Bayesian active learning with EPIG data acquisition
  ☆25Updated 5 months ago
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆37Updated last year
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆18Updated 5 years ago
• gibipara92 / learning-explanations-hard-to-vary
  Code to implement the AND-mask and geometric mean to do gradient based optimization, from the paper "Learning explanations that are hard …
  ☆39Updated 3 years ago
• SebFar / radial_bnn
  Code to accompany the paper Radial Bayesian Neural Networks: Beyond Discrete Support In Large-Scale Bayesian Deep Learning
  ☆33Updated 4 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆55Updated 3 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆80Updated 4 months ago
• team-approx-bayes / kpriors
  Code for Knowledge-Adaptation Priors based on the NeurIPS 2021 paper by Khan and Swaroop.
  ☆16Updated 2 years ago
• wiseodd / last_layer_laplace
  Last-layer Laplace approximation code examples
  ☆80Updated 3 years ago
• facebookresearch / BalancingGroups
  Simple data balancing baselines for worst-group-accuracy benchmarks.
  ☆40Updated last year
• activatedgeek / svgd
  PyTorch implementation of Stein Variational Gradient Descent
  ☆41Updated last year
• dtak / ocbnn-public
  General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
  ☆38Updated 2 years ago
• locuslab / monotone_op_net
  Monotone operator equilibrium networks
  ☆51Updated 4 years ago
• fKunstner / limitations-empirical-fisher
  Limitations of the Empirical Fisher Approximation
  ☆45Updated 4 years ago
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 4 years ago
• g-benton / loss-surface-simplexes
  ☆97Updated 2 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆30Updated 2 years ago
• ssydasheng / FBNN
  Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)
  ☆80Updated 4 years ago
• lfbo-ml / lfbo
  Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022
  ☆44Updated 2 years ago
• g-benton / learning-invariances
  Codebase for Learning Invariances in Neural Networks
  ☆94Updated 2 years ago