Alternatives and similar repositories for W-VI:

Users that are interested in W-VI are comparing it to the libraries listed below

• FrederikWarburg / LaplaceAE
  ☆32Updated 2 years ago
• cbg-ethz / bmi
  Mutual information estimators and benchmark
  ☆44Updated last month
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 5 years ago
• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• ilkhem / icebeem
  Code for ICE-BeeM paper - NeurIPS 2020
  ☆86Updated 3 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆70Updated 2 years ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆21Updated 2 years ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆32Updated 2 years ago
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆52Updated 4 years ago
• VectorInstitute / vbll
  Simple (and cheap!) neural network uncertainty estimation
  ☆61Updated 3 weeks ago
• AaltoML / sequential-gp
  Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
  ☆12Updated last year
• ae-foster / pyro
  Deep universal probabilistic programming with Python and PyTorch
  ☆11Updated 4 years ago
• CUAI / Riemannian-Residual-Neural-Networks
  [NeurIPS 2023] Riemannian Residual Neural Networks (https://arxiv.org/abs/2006.10254)
  ☆18Updated last year
• akekic / causal-component-analysis
  ☆23Updated last year
• AmirTag / OT-ICNN
  Learning the optimal transport map via input convex neural neworks
  ☆41Updated 4 years ago
• judelo / gmmot
  Python notebooks for Optimal Transport between Gaussian Mixture Models
  ☆41Updated 3 years ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆97Updated 10 months ago
• vislearn / GIN
  Code for the paper "Disentanglement by Nonlinear ICA with General Incompressible-flow Networks (GIN)" (2020)
  ☆32Updated 3 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆78Updated 3 years ago
• activatedgeek / understanding-bayesian-classification
  On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification
  ☆21Updated 2 years ago
• ngushchin / EntropicNeuralOptimalTransport
  Pytorch implementation of "Entropic Neural Optimal Transport via Diffusion Processes" (NeurIPS 2023, oral).
  ☆36Updated 11 months ago
• MachineLearningLifeScience / stochman
  Algorithms for computations on random manifolds made easier
  ☆87Updated last year
• noamroze / moser_flow
  ☆16Updated 2 years ago
• fbickfordsmith / epig
  Bayesian active learning with EPIG data acquisition
  ☆28Updated last month
• MichaelArbel / MMD-gradient-flow
  ☆12Updated 7 months ago
• mfederici / torch-mist
  Implementation of a PyTorch Mutual Information Estimation Toolkit
  ☆15Updated 11 months ago
• ilkhem / iVAE
  VAEs and nonlinear ICA: a unifying framework
  ☆33Updated 4 years ago
• ifiaposto / Distance_Aware_Bottleneck
  Official Implementation of the paper: "A Rate-Distorion View of Uncertainty Quantification", ICML 2024
  ☆28Updated 6 months ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆51Updated 7 months ago
• ratschlab / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago