Alternatives and similar repositories for template_benchmark:

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

• lindermanlab / ssm-jax
  Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend
  ☆58Updated 9 months ago
• aterenin / SparseGPAX
  An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.
  ☆22Updated 4 years ago
• josephsalmon / OrganizationFiles
  ☆13Updated last month
• hichamjanati / mutar
  Multi-task regression in Python
  ☆25Updated 4 years ago
• agramfort / DS3_practical_optim_for_ml
  Notebooks from DS3 course on practical optimization
  ☆15Updated 4 years ago
• QB3 / sparse-ho
  Fast hyperparameter settings for non-smooth estimators:
  ☆39Updated last year
• wesselb / lab
  A generic interface for linear algebra backends
  ☆73Updated last month
• probml / JSL
  Jax SSM Library
  ☆49Updated 2 years ago
• jeremiecoullon / SGMCMCJax
  Lightweight library of stochastic gradient MCMC algorithms written in JAX.
  ☆102Updated last year
• IPL-UV / jaxkern
  A lightweight didactic library of kernel methods using the back-end JAX.
  ☆12Updated 2 years ago
• pierreablin / ksddescent
  Kernel Stein Discrepancy Descent : a method to sample from unnormalized densities
  ☆21Updated 11 months ago
• benchopt / benchopt
  Making your benchmark of optimization algorithms simple and open
  ☆261Updated 3 months ago
• NeilGirdhar / tjax
  Tools for JAX
  ☆45Updated last week
• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆43Updated last year
• andres-fr / skerch
  Sketched matrix decompositions for PyTorch
  ☆70Updated 2 weeks ago
• f-dangel / phd-thesis
  Source code for my PhD thesis: Backpropagation Beyond the Gradient
  ☆20Updated 2 years ago
• EEA-sensors / sequential-parallelization-examples
  This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.
  ☆31Updated last year
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆98Updated last year
• mathurinm / andersoncd
  This code is no longer maintained. The codebase has been moved to https://github.com/scikit-learn-contrib/skglm. This repository only ser…
  ☆17Updated 2 years ago
• f-dangel / curvlinops
  PyTorch linear operators for curvature matrices (Hessian, Fisher/GGN, KFAC, ...)
  ☆34Updated last month
• baumgach / tue-slurm-helloworld
  Instructions and examples to deploy some PyTorch code on slurm using a Singularity Container
  ☆33Updated 2 years ago
• PerformanceEstimation / PEPit
  PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
  ☆85Updated 2 months ago
• facebookresearch / rcpm
  Riemannian Convex Potential Maps
  ☆67Updated 2 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆41Updated 8 months ago
• acerbilab / pyvbmc
  PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python
  ☆117Updated 2 months ago
• MachineLearningLifeScience / stochman
  Algorithms for computations on random manifolds made easier
  ☆89Updated last year
• vr308 / Generalised-Gaussian-Processes
  Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods
  ☆22Updated last year
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆35Updated 2 years ago
• bremen79 / parameterfree
  Parameter-Free Optimizers for Pytorch
  ☆122Updated 11 months ago
• cunningham-lab / cb_and_cc
  ☆36Updated 2 years ago