Alternatives and similar repositories for you-need-a-good-prior

Users that are interested in you-need-a-good-prior are comparing it to the libraries listed below

• aleximmer / BNN-predictions
  Improving predictions of Bayesian neural nets via local linearization, AISTATS 2021
  ☆16Updated 2 years ago
• dngfra / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• team-approx-bayes / ivon
  IVON optimizer for neural networks based on variational learning.
  ☆70Updated 9 months ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆107Updated last year
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆81Updated last year
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆73Updated 4 years ago
• ludwigwinkler / pytorch_MCMC
  Lightweight MCMC sampling for PyTorch Models aka My Corona Project
  ☆49Updated 2 weeks ago
• yucenli / bnn-bo
  Bayesian Neural Network Surrogates for Bayesian Optimization
  ☆55Updated last year
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆89Updated 4 years ago
• YannDubs / Neural-Process-Family
  Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
  ☆223Updated last year
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆47Updated 2 years ago
• DanieleGammelli / pyro-torch-normalizing-flows
  Implementations of Normalizing Flows in Pytorch/Pyro
  ☆19Updated 5 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆74Updated 3 years ago
• tung-nd / TNP-pytorch
  Official implementation of Transformer Neural Processes
  ☆78Updated 3 years ago
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated 2 months ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆20Updated 3 years ago
• TyXe-BDL / TyXe
  ☆152Updated 2 years ago
• Feuermagier / Beyond_Deep_Ensembles
  Code for the paper "Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift"
  ☆22Updated last year
• fbickfordsmith / epig
  Bayesian active learning with EPIG data acquisition
  ☆34Updated 4 months ago
• VectorInstitute / vbll
  Simple (and cheap!) neural network uncertainty estimation
  ☆69Updated 3 months ago
• wesselb / neuralprocesses
  A framework for composing Neural Processes in Python
  ☆85Updated 8 months ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year
• martius-lab / beta-nll
  beta-NLL introduced in our paper "On the Pitfalls of Heteroscedastic Uncertainty Estimation with Probabilistic Neural Networks" ICLR 2022
  ☆46Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆57Updated 4 years ago