Alternatives and similar repositories for W-VI

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

• federicobergamin / riemannian-laplace-approximation
  Repository for the paper "Riemannian Laplace approximations for Bayesian neural networks"
  ☆11Updated last year
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆32Updated 3 years ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆21Updated 3 years ago
• angusphillips / particle_denoising_diffusion_sampler
  Code repository for Particle Denoising Diffusion Sampler
  ☆14Updated last year
• MichaelArbel / MMD-gradient-flow
  ☆12Updated 9 months ago
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆52Updated 4 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆72Updated 3 years ago
• AaltoML / sequential-gp
  Code for 'Memory-based dual Gaussian processes for sequential learning' (ICML 2023)
  ☆12Updated last year
• judelo / gmmot
  Python notebooks for Optimal Transport between Gaussian Mixture Models
  ☆45Updated 4 years ago
• FrederikWarburg / LaplaceAE
  ☆32Updated 2 years ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆100Updated last year
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆12Updated 3 years ago
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆77Updated 9 months ago
• ludwigwinkler / pytorch_MCMC
  Lightweight MCMC sampling for PyTorch Models aka My Corona Project
  ☆48Updated 4 years ago
• jongharyu / neural-svd
  ☆16Updated 8 months ago
• ilkhem / icebeem
  Code for ICE-BeeM paper - NeurIPS 2020
  ☆87Updated 3 years ago
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆52Updated 9 months ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆77Updated 3 years ago
• FabianAltekrueger / NeuralWassersteinGradientFlows
  Implementation of the paper "Neural Wasserstein Gradient Flows for Discrepancies with Riesz Kernels"
  ☆11Updated last year
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆23Updated 2 years ago
• chenw20 / SGPA
  Example code of Sparse Gaussian Process Attention (ICLR 2023)
  ☆24Updated 10 months ago
• ngushchin / EntropicNeuralOptimalTransport
  Pytorch implementation of "Entropic Neural Optimal Transport via Diffusion Processes" (NeurIPS 2023, oral).
  ☆37Updated last year
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated 2 years ago
• MachineLearningLifeScience / stochman
  Algorithms for computations on random manifolds made easier
  ☆89Updated last year
• fbickfordsmith / epig
  Bayesian active learning with EPIG data acquisition
  ☆31Updated last month
• vdeborto / cdsb
  ☆23Updated 2 years ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• sbyebss / variational_wgf
  [ICML2022] Variational Wasserstein gradient flow
  ☆22Updated 2 years ago
• bmucsanyi / untangle
  Large-scale uncertainty benchmark in deep learning.
  ☆57Updated last week