Alternatives and similar repositories for large-scale-material

Users that are interested in large-scale-material are comparing it to the libraries listed below

• JonathanWenger / itergp
  IterGP: Computation-Aware Gaussian Process Inference (NeurIPS 2022)
  ☆42Updated 2 years ago
• probml / JSL
  Jax SSM Library
  ☆49Updated 2 years ago
• AaltoML / kalman-jax
  Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
  ☆100Updated 2 years ago
• google-deepmind / PGMax
  Loopy belief propagation for factor graphs on discrete variables in JAX
  ☆154Updated 9 months ago
• EEA-sensors / sequential-parallelization-examples
  This is a collection of code samples aimed at illustrating temporal parallelization methods for sequential data.
  ☆33Updated last year
• YinTat / optimizationbook
  ☆142Updated 2 months ago
• probml / rebayes
  Recursive Bayesian Estimation (Sequential / Online Inference)
  ☆59Updated last year
• vicariousinc / PGMax
  Loopy belief propagation for factor graphs on discrete variables, in JAX!
  ☆65Updated 10 months ago
• fbach2000 / Learning_Theory_from_First_Principles
  Python & Matlab code for the figures from the book "Learning Theory from First Principles" by Francis Bach
  ☆118Updated last year
• wjmaddox / online_gp
  Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"
  ☆64Updated 4 years ago
• AaltoML / BayesNewton
  Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
  ☆235Updated last year
• EEA-sensors / parallel-non-linear-gaussian-smoothers
  Companion code in JAX for the paper Parallel Iterated Extended and Sigma-Point Kalman Smoothers.
  ☆27Updated last year
• mossr / TikzNeuralNetworks.jl
  Visualize neural networks using TikZ in Julia
  ☆15Updated 6 months ago
• QuantEcon / lecture-jax
  Lectures on Quantitative Economics Using JAX
  ☆38Updated this week
• PerformanceEstimation / PEPit
  PEPit is a package enabling computer-assisted worst-case analyses of first-order optimization methods.
  ☆88Updated this week
• imbirik / LieStationaryKernels
  Supplementary code for the paper "Stationary Kernels and Gaussian Processes on Lie Groups and their Homogeneous Spaces"
  ☆43Updated last year
• ExpectationMax / sklearn-jax-kernels
  Composable kernels for scikit-learn implemented in JAX.
  ☆44Updated 4 years ago
• SaitejaUtpala / rieoptax
  Riemannian Optimization Using JAX
  ☆51Updated last year
• sisl / AA120Q
  AA120Q Course Materials
  ☆29Updated 6 months ago
• bodono / scs-python
  Python interface for SCS
  ☆43Updated this week
• cagatayyildiz / npde
  Nonparametric Differential Equation Modeling
  ☆54Updated last year
• gerdm / bayes
  Neat Bayesian machine learning examples
  ☆58Updated last month
• cvxgrp / diffcp
  Differentiation through cone programs
  ☆102Updated 3 weeks ago
• erichanslee / GP_Derivatives
  Scalable Gaussian Process Regression with Derivatives
  ☆37Updated 6 years ago
• wjmaddox / online_vargp
  Online variational GPs
  ☆37Updated 2 years ago
• udellgroup / orie7391
  Materials and syllabus for Cornell ORIE 7391, Faster: Algorithmic Ideas for Speeding Up Optimization
  ☆25Updated 3 years ago
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆171Updated 3 years ago
• ghliu / snopt
  Second-Order Neural ODE Optimizer, NeurIPS 2021 spotlight
  ☆52Updated 3 years ago
• EEA-sensors / Bayesian-Filtering-and-Smoothing
  Companion Matlab and Python codes for the book Bayesian Filtering and Smoothing by Simo Särkkä and Lennart Svensson
  ☆75Updated last year
• JTT94 / filterflow
  Extensible Tensorflow library for differentiable particle filtering. ICML 2021.
  ☆42Updated 2 years ago