Alternatives and similar repositories for bayesian_ood_training

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

• MichaelArbel / GeneralizedEBM
  ☆54Updated 11 months ago
• izmailovpavel / bnn_covariate_shift
  Supporting code for the paper "Dangers of Bayesian Model Averaging under Covariate Shift"
  ☆33Updated 2 years ago
• mfederici / dsit
  Implementation of the models and datasets used in "An Information-theoretic Approach to Distribution Shifts"
  ☆25Updated 3 years ago
• ermongroup / f-dre
  Featurized Density Ratio Estimation
  ☆20Updated 4 years ago
• AVMCMC / AuxiliaryVariationalMCMC
  Code required to reproduce the experiments in Auxiliary Variational MCMC
  ☆17Updated 6 years ago
• cambridge-mlg / neural_diffusion_processes
  ☆13Updated 2 years ago
• aangelopoulos / private_prediction_sets
  Wrap around any model to output differentially private prediction sets with finite sample validity on any dataset.
  ☆18Updated last year
• a7b23 / H-Divergence
  ☆21Updated 3 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆32Updated 3 years ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆76Updated last year
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆77Updated 3 years ago
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• dtak / ocbnn-public
  General purpose library for BNNs, and implementation of OC-BNNs in our 2020 NeurIPS paper.
  ☆38Updated 3 years ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• siplab-gt / VAELLS
  Code for "Variational Autoencoder with Learned Latent Structure"
  ☆34Updated 4 years ago
• wgrathwohl / LSD
  Official Release of "Learning the Stein Discrepancy for Training and Evaluating Energy-Based Models without Sampling"
  ☆49Updated 5 years ago
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆36Updated 3 years ago
• wgrathwohl / VERA
  ☆63Updated last year
• justkolesov / Wasserstein1Benchmark
  A set of tests for evaluating large-scale algorithms for Wasserstein-1 transport computation (NeurIPS'22).
  ☆21Updated 10 months ago
• federicobergamin / Variational-Inference-with-Normalizing-Flows
  Personal implementation of "Variational Inference with Normalizing Flows" by [Rezende, et al., 2015] in PyTorch
  ☆22Updated 5 years ago
• ykwon0407 / wdro_local_perturbation
  Principled learning method for Wasserstein distributionally robust optimization with local perturbations (ICML 2020)
  ☆21Updated 2 years ago
• mariushobbhahn / LB_for_BNNs_official
  Official repository for the paper "Fast Predictive Uncertainty for Classification with Bayesian Deep Networks". Accepted at UAI 2022. htt…
  ☆12Updated 3 years ago
• akekic / causal-component-analysis
  ☆25Updated last year
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• kakaobrain / irm-empirical-study
  An Empirical Study of Invariant Risk Minimization
  ☆27Updated 5 years ago
• mrojascarulla / causal_transfer_learning
  ☆32Updated 7 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated last month
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆20Updated 3 years ago
• martius-lab / beta-nll
  beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022
  ☆45Updated 3 years ago