Alternatives and similar repositories for sequential-gp:

Users that are interested in sequential-gp are comparing it to the libraries listed below

• tranbahien / you-need-a-good-prior
  All You Need is a Good Functional Prior for Bayesian Deep Learning (JMLR 2022)
  ☆21Updated 2 years ago
• ratschlab / repulsive_ensembles
  Repo for our paper "Repulsive deep ensembles are Bayesian"
  ☆19Updated 3 years ago
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆52Updated 4 years ago
• plugyawn / gp-zoo
  A repository with implementations of major papers on Gaussian Process regression models, implemented from scratch in Python, notably incl…
  ☆14Updated 2 years ago
• VectorInstitute / vbll
  Simple (and cheap!) neural network uncertainty estimation
  ☆61Updated 3 weeks ago
• Machine-Learning-Dynamical-Systems / kooplearn
  A Python package to learn the Koopman operator.
  ☆53Updated 3 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…
  ☆11Updated 2 years ago
• alisiahkoohi / Langevin-dynamics
  Sampling with gradient-based Markov Chain Monte Carlo approaches
  ☆97Updated 10 months ago
• runame / laplace-refinement
  Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks
  ☆10Updated 2 years ago
• samholt / NeuralLaplace
  Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
  ☆75Updated last week
• cambridge-mlg / neural_diffusion_processes
  ☆13Updated last year
• jongharyu / neural-svd
  ☆16Updated 5 months ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆41Updated 7 months ago
• jejjohnson / uncertain_gps
  Exploring how to to deal with uncertain inputs with gaussian process regression models.
  ☆27Updated 3 years ago
• Feuermagier / Beyond_Deep_Ensembles
  Code for the paper "Beyond Deep Ensembles: A Large-Scale Evaluation of Bayesian Deep Learning under Distribution Shift"
  ☆22Updated last year
• dmusekamp / al4pde
  AL4PDE: A Benchmark for Active Learning for Neural PDE Solvers
  ☆17Updated this week
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆42Updated last year
• vdutor / neural-diffusion-processes
  Neural Diffusion Processes
  ☆76Updated 7 months ago
• AaltoML / scalable-inference-in-sdes
  Methods and experiments for assumed density SDE approximations
  ☆11Updated 3 years ago
• facebookresearch / SOC-matching
  Code release for "Stochastic Optimal Control Matching"
  ☆30Updated 6 months ago
• PetrMokrov / Large-Scale-Wasserstein-Gradient-Flows
  Source code for Large-Scale Wasserstein Gradient Flows (NeurIPS 2021)
  ☆32Updated 2 years ago
• desi-ivanova / idad
  Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods
  ☆18Updated 3 years ago
• noamroze / moser_flow
  ☆16Updated 2 years ago
• secondmind-labs / markovflow
  A Tensorflow based library for Time Series Modelling with Gaussian Processes
  ☆29Updated 8 months ago
• mishapadidar / GP-Derivatives-Variational-Inference
  ☆14Updated 3 years ago
• jonasrothfuss / f-pacoh-torch
  ☆9Updated 2 years ago
• team-approx-bayes / ivon
  IVON optimizer for neural networks based on variational learning.
  ☆59Updated 3 months ago
• LaurenceA / bayesfunc
  ☆15Updated 2 years ago
• jmaronas / TGP.pytorch
  Repository for the work Transforming Gaussian Processes with Normalizing Flows published at AISTATS 2021
  ☆24Updated 2 years ago
• fidero / gp-derivative
  Example code to speed up GP inference with gradients for high-dimensional inputs.
  ☆14Updated 3 years ago