Alternatives and similar repositories for WGPOT

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

• judelo / gmmot
  Python notebooks for Optimal Transport between Gaussian Mixture Models
  ☆46Updated 4 years ago
• cambridge-mlg / neural_diffusion_processes
  ☆13Updated 2 years ago
• sharpenb / Posterior-Network
  Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts (Neurips 2020)
  ☆78Updated 3 years ago
• lfbo-ml / lfbo
  Code for A General Recipe for Likelihood-free Bayesian Optimization, ICML 2022
  ☆45Updated 3 years ago
• borchero / natural-posterior-network
  Official Implementation of "Natural Posterior Network: Deep Bayesian Predictive Uncertainty for Exponential Family Distributions" (ICLR, …
  ☆87Updated 2 years ago
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆99Updated 3 years ago
• Jiacheng-Zhu-AIML / FOT
  Functional Optimal Transport: Map Estimation and Domain Adaptation for Functional data
  ☆26Updated 4 years ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆78Updated 2 years ago
• bobby-he / bayesian-ntk
  Code to accompany paper 'Bayesian Deep Ensembles via the Neural Tangent Kernel'
  ☆26Updated 4 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆75Updated 3 years ago
• siplab-gt / VAELLS
  Code for "Variational Autoencoder with Learned Latent Structure"
  ☆34Updated 4 years ago
• a-norcliffe / sonode
  Experiments from the paper "On Second Order Behaviour in Augmented Neural ODEs"
  ☆59Updated last year
• iamalexkorotin / Wasserstein2Barycenters
  PyTorch implementation of the paper "Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization" (ICLR 2021)
  ☆24Updated 3 years ago
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆47Updated 2 years ago
• spbu-math-cs / Riemannian-Gaussian-Processes
  Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".
  ☆29Updated 9 months ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆81Updated 6 months ago
• locuslab / JIIO-DEQ
  Efficient joint input optimization and inference with DEQ
  ☆10Updated 3 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆90Updated 5 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆58Updated 4 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆78Updated 4 years ago
• ChengzijunAixiaoli / PPMM
  Python3 implementation of the paper [Large-scale optimal transport map estimation using projection pursuit]
  ☆15Updated 4 years ago
• NoemieJaquier / GaBOtorch
  This repository contains the source code to perform Geometry-aware Bayesian Optimization (GaBO) on Riemannian manifolds.
  ☆53Updated 3 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
• FrederikWarburg / LaplaceAE
  ☆32Updated 3 years ago
• juangamella / aicp
  Code to reproduce the experimental results from the paper "Active Invariant Causal Prediction: Experiment Selection Through Stability", b…
  ☆21Updated 2 years ago
• facebookresearch / amortized-optimization-tutorial
  Tutorial on amortized optimization for learning to optimize over continuous domains
  ☆247Updated last month
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆37Updated 3 years ago
• AmirTag / OT-ICNN
  Learning the optimal transport map via input convex neural neworks
  ☆42Updated 5 years ago
• cambridge-mlg / CLUE
  Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"
  ☆36Updated last year
• team-approx-bayes / dnn2gp
  Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)
  ☆48Updated 5 years ago