Alternatives and similar repositories for riemannian-laplace-approximation

Users that are interested in riemannian-laplace-approximation are comparing it to the libraries listed below

• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆32Updated 3 years ago
• JeremiasKnoblauch / GVIPublic
  Demos for the paper Generalized Variational Inference (Knoblauch, Jewson & Damoulas, 2019)
  ☆20Updated 6 years ago
• FrederikWarburg / LaplaceAE
  ☆32Updated 2 years ago
• ludwigwinkler / pytorch_MCMC
  Lightweight MCMC sampling for PyTorch Models aka My Corona Project
  ☆48Updated last week
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated 2 weeks ago
• MichaelArbel / MMD-gradient-flow
  ☆12Updated 11 months ago
• bmucsanyi / untangle
  Large-scale uncertainty benchmark in deep learning.
  ☆60Updated last month
• VincentStimper / resampled-base-flows
  Normalizing Flows with a resampled base distribution
  ☆47Updated 2 years ago
• fbickfordsmith / epig
  Bayesian active learning with EPIG data acquisition
  ☆32Updated 2 months ago
• cyz-ai / neural-approx-ss-lfi
  Codes for ICLR 21 paper: Neural Approximate Sufficient Statistics for Implicit Models
  ☆19Updated 3 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 3 years ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆20Updated 3 years ago
• dngfra / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• f-dangel / curvlinops
  PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)
  ☆40Updated 2 months ago
• pmorenoz / ModularGP
  Modular Gaussian Processes
  ☆15Updated 3 years ago
• PrincetonLIPS / AHGP
  [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)
  ☆22Updated 4 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆19Updated 3 years ago
• wjmaddox / spectralgp
  Code repo for "Function-Space Distributions over Kernels"
  ☆31Updated 4 years ago
• DanieleGammelli / pyro-torch-normalizing-flows
  Implementations of Normalizing Flows in Pytorch/Pyro
  ☆19Updated 5 years ago
• vdeborto / cdsb
  ☆23Updated 2 years ago
• f-dangel / phd-thesis
  Source code for my PhD thesis: Backpropagation Beyond the Gradient
  ☆20Updated 2 years ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• chenw20 / SGPA
  Example code of Sparse Gaussian Process Attention (ICLR 2023)
  ☆24Updated 11 months ago
• marc-h-lambert / W-VI
  The companion code for the paper "Variational inference via Wasserstein gradient flows (W-VI) M. Lambert, S. Chewi, F. Bach, S. Bonnabel…
  ☆14Updated 2 years ago
• layer6ai-labs / two_step_zoo
  Codebase accompanying the paper "Diagnosing and Fixing Manifold Overfitting in Deep Generative Models"
  ☆9Updated 10 months ago
• LaurantChao / VIP
  Variational Implicit Processes
  ☆9Updated 6 years ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆103Updated last year
• bunnech / gwgan
  Learning Generative Models across Incomparable Spaces (ICML 2019)
  ☆27Updated 5 years ago