Alternatives and similar repositories for VINF

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

• patrick-kidger / FasterNeuralDiffEq
  Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
  ☆87Updated 2 years ago
• wjmaddox / spectralgp
  Code repo for "Function-Space Distributions over Kernels"
  ☆32Updated 4 years ago
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated 2 years ago
• Information-Fusion-Lab-Umass / NuX
  Normalizing Flows using JAX
  ☆84Updated last year
• CUAI / Neural-Manifold-Ordinary-Differential-Equations
  [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)
  ☆119Updated 2 years ago
• microsoft / vi-hds
  Variational inference for hierarchical dynamical systems
  ☆48Updated last year
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• hegdepashupati / differential-dgp
  Implementation of stochastic variational inference for differentially deep gaussian processes
  ☆15Updated 6 years ago
• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 3 years ago
• karalets / TyXe
  Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)
  ☆42Updated 3 years ago
• j-wilson / GPflowSampling
  Code for efficiently sampling functions from GP(flow) posteriors
  ☆72Updated 4 years ago
• siboehm / NormalizingFlowNetwork
  Bayesian and Maximum Likelihood Implementation of the Normalizing Flow Network (NFN): https://arxiv.org/abs/1907.08982
  ☆21Updated 4 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆73Updated 3 years ago
• conormdurkan / lfi
  Neural likelihood-free methods in PyTorch.
  ☆39Updated 5 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 6 years ago
• noahgolmant / SGLD
  Stochastic Gradient Langevin Dynamics for Bayesian learning
  ☆34Updated 3 years ago
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago
• wjmaddox / drbayes
  Code Repo for "Subspace Inference for Bayesian Deep Learning"
  ☆82Updated last year
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated 2 months ago
• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆121Updated 3 years ago
• robert-giaquinto / gradient-boosted-normalizing-flows
  We got a stew going!
  ☆27Updated last year
• probml / JSL
  Jax SSM Library
  ☆49Updated 2 years ago
• uber-research / vargp
  Variational Auto-Regressive Gaussian Processes for Continual Learning
  ☆21Updated 4 years ago
• Tom-Ryder / VIforSDEs
  A variational method for fast, approximate inference for stochastic differential equations.
  ☆44Updated 7 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆20Updated 3 years ago
• pmorenoz / RecyclableGP
  Recyclable Gaussian Processes
  ☆11Updated 2 years ago
• AaltoML / boundary-gp
  Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features
  ☆23Updated 6 years ago
• hughsalimbeni / DGPs_with_IWVI
  Deep Gaussian Processes with Importance-Weighted Variational Inference
  ☆39Updated 6 years ago
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆100Updated 2 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆57Updated 4 years ago