Alternatives and similar repositories for goodpoints:

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

• runame / laplace-redux
  Laplace Redux -- Effortless Bayesian Deep Learning
  ☆43Updated last year
• bremen79 / parameterfree
  Parameter-Free Optimizers for Pytorch
  ☆122Updated 11 months ago
• wiseodd / pub-ready-plots
  Agustinus' very opiniated publication-ready plotting library
  ☆62Updated 2 months ago
• tk-rusch / unicornn
  Official code for UnICORNN (ICML 2021)
  ☆27Updated 3 years ago
• tml-epfl / sgd-sparse-features
  SGD with large step sizes learns sparse features [ICML 2023]
  ☆32Updated last year
• karalets / TyXe
  Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)
  ☆42Updated 3 years ago
• EdinburghNLP / torch-adaptive-imle
  ☆34Updated 3 months ago
• jongharyu / neural-svd
  ☆16Updated 6 months ago
• rgklab / StructuredNNs
  Structured Neural Networks
  ☆14Updated 10 months ago
• Information-Fusion-Lab-Umass / NuX
  Normalizing Flows using JAX
  ☆83Updated last year
• MichaelArbel / GeneralizedEBM
  ☆53Updated 8 months ago
• IPL-UV / jaxkern
  A lightweight didactic library of kernel methods using the back-end JAX.
  ☆12Updated 2 years ago
• KordingLab / hilbert-constrained-gradient-descent
  Pytorch optimizers implementing Hilbert Constrained Gradient Descent
  ☆19Updated 5 years ago
• Harshs27 / neural-graphical-models
  Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling
  ☆28Updated last year
• dylandoblar / noether-networks
  Meta-learning inductive biases in the form of useful conserved quantities.
  ☆37Updated 2 years ago
• jmtomczak / git_flow
  General Invertible Transformations for Flow-based Generative Models
  ☆17Updated 4 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆77Updated 3 years ago
• aks2203 / easy-to-hard
  Official repository for the paper "Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks"
  ☆59Updated 3 years ago
• michaelsdr / sinkformers
  Transformers with doubly stochastic attention
  ☆45Updated 2 years ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆41Updated 8 months ago
• probabilists / lampe
  Likelihood-free AMortized Posterior Estimation with PyTorch
  ☆126Updated 7 months ago
• KarenUllrich / binary-VAE
  A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.
  ☆21Updated 5 years ago
• ssokota / mec
  Code for minimum-entropy coupling.
  ☆31Updated 9 months ago
• AhmedImtiazPrio / grok-adversarial
  Deep Networks Grok All the Time and Here is Why
  ☆33Updated 10 months ago
• nec-research / tf-imle
  Tensorflow implementation and notebooks for Implicit Maximum Likelihood Estimation
  ☆67Updated 3 years ago
• google-deepmind / dks
  Multi-framework implementation of Deep Kernel Shaping and Tailored Activation Transformations, which are methods that modify neural netwo…
  ☆68Updated last week
• lixilinx / psgd_torch
  Pytorch implementation of preconditioned stochastic gradient descent (Kron and affine preconditioner, low-rank approximation precondition…
  ☆171Updated this week
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆46Updated last year
• js-d / sim_metric
  ☆35Updated last year
• teddykoker / grokking
  PyTorch implementation of "Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets"
  ☆36Updated 3 years ago