Alternatives and similar repositories for torchplot

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

• danielegrattarola / GINR
  Code for "Generalised Implicit Neural Representations" (NeurIPS 2022).
  ☆70Updated 2 years ago
• rajesh-lab / deep_permutation_invariant
  Code accompanying paper "Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets."
  ☆29Updated 3 years ago
• TorchUQ / torchuq
  A library for uncertainty quantification based on PyTorch
  ☆122Updated 3 years ago
• cscarv / riemannian-metric-learning-ot
  ☆17Updated 2 years ago
• borchero / pycave
  Traditional Machine Learning Models for Large-Scale Datasets in PyTorch.
  ☆128Updated last week
• chingyaoc / TMD
  NeurIPS 2022: Tree Mover’s Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
  ☆37Updated 2 years ago
• ehoogeboom / convolution_exponential_and_sylvester
  ☆28Updated 3 years ago
• mfinzi / olive-oil-ml
  ☆14Updated 2 years ago
• Lightning-Universe / paper-AAVAE
  ☆47Updated 2 years ago
• fsschneider / cockpit-experiments
  Experiments for the NeurIPS 2021 paper "Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks"
  ☆13Updated 4 years ago
• hchau630 / LSC
  Code for "Disentangling images with Lie group transformations and sparse coding" (2023).
  ☆13Updated 4 years ago
• Sanaelotfi / Bayesian_model_comparison
  Supporing code for the paper "Bayesian Model Selection, the Marginal Likelihood, and Generalization".
  ☆37Updated 3 years ago
• CUAI / Neural-Manifold-Ordinary-Differential-Equations
  [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)
  ☆121Updated 2 years ago
• cambridge-mlg / convcnp
  Implementation of the Convolutional Conditional Neural Process
  ☆127Updated 4 years ago
• stanfordmlgroup / disentanglement
  Official repository for our ICLR 2021 paper Evaluating the Disentanglement of Deep Generative Models with Manifold Topology
  ☆36Updated 4 years ago
• maxxxzdn / implicit-steerable-kernels
  Implicit Convolutional Kernels for Steerable CNNs [NeurIPS'23]
  ☆29Updated 8 months ago
• g-benton / learning-invariances
  Codebase for Learning Invariances in Neural Networks
  ☆96Updated 3 years ago
• adrianjav / heterogeneous_vaes
  Pytorch implementation of VAEs for heterogeneous likelihoods.
  ☆43Updated 3 years ago
• wgrathwohl / VERA
  ☆64Updated last year
• facebookresearch / w2ot
  Euclidean Wasserstein-2 optimal transportation
  ☆47Updated 2 years ago
• facebookresearch / irmae
  PyTorch implementation of IRMAE https//arxiv.org/abs/2010.00679
  ☆48Updated 3 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆78Updated 4 years ago
• ParthaEth / Regularized_autoencoders-RAE-
  ☆125Updated 2 years ago
• nicola-decao / power_spherical
  Pytorch implementation of the Power Spherical distribution
  ☆74Updated last year
• tychovdo / lila
  Code for "Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations"
  ☆23Updated 3 years ago
• MJ10 / DEUP
  Code for experiments to learn uncertainty
  ☆30Updated 2 years ago
• AhmedImtiazPrio / grok-adversarial
  Deep Networks Grok All the Time and Here is Why
  ☆37Updated last year
• EmoryMLIP / OT-Flow
  PyTorch implementation of the OT-Flow approach in arXiv:2006.00104
  ☆57Updated last year
• SamsungSAILMontreal / PAPA
  Repository for the PopulAtion Parameter Averaging (PAPA) paper
  ☆28Updated last year
• NVlabs / VAEBM
  The Official PyTorch Implementation of "VAEBM: A Symbiosis between Variational Autoencoders and Energy-based Models" (ICLR 2021 spotlight…
  ☆57Updated 3 years ago