Alternatives and similar repositories for keras-lipschitz-networks

Users that are interested in keras-lipschitz-networks are comparing it to the libraries listed below

• fpcasale / GPPVAE
  Gaussian Process Prior Variational Autoencoder
  ☆87Updated 7 years ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆131Updated last year
• CUAI / Neural-Manifold-Ordinary-Differential-Equations
  [NeurIPS 2020] Neural Manifold Ordinary Differential Equations (https://arxiv.org/abs/2006.10254)
  ☆125Updated 2 years ago
• LCAS / RKN
  ☆47Updated 3 years ago
• mctorch / mctorch
  A manifold optimization library for deep learning
  ☆251Updated 4 years ago
• siplab-gt / VAELLS
  Code for "Variational Autoencoder with Learned Latent Structure"
  ☆34Updated 4 years ago
• georgiosarvanitidis / geometric_ml
  This repository contains code for applying Riemannian geometry in machine learning.
  ☆78Updated 4 years ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆76Updated 3 years ago
• cambridge-mlg / DUN
  Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
  ☆78Updated 2 years ago
• cambridge-mlg / cnn-gp
  Pytorch version of "Deep Convolutional Networks as shallow Gaussian Processes" by Adrià Garriga-Alonso, Carl Rasmussen and Laurence Aitch…
  ☆32Updated 5 years ago
• patrick-kidger / FasterNeuralDiffEq
  Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
  ☆89Updated 3 years ago
• skolouri / swgmm
  Sliced Wasserstein Distance for Learning Gaussian Mixture Models
  ☆66Updated 2 years ago
• dimkal89 / rvae
  ☆37Updated 5 years ago
• Jiacheng-Zhu-AIML / WGPOT
  The Wasserstein Distance and Optimal Transport Map of Gaussian Processes
  ☆52Updated 5 years ago
• massastrello / awesome-implicit-neural-models
  ☆105Updated 4 years ago
• mfinzi / LieConv
  ☆274Updated 2 years ago
• master / tensorflow-riemopt
  A library for optimization on Riemannian manifolds
  ☆112Updated last month
• y0ast / deterministic-uncertainty-quantification
  Code for "Uncertainty Estimation Using a Single Deep Deterministic Neural Network"
  ☆275Updated 3 years ago
• hichamjanati / spatio-temporal-alignements
  Spatio-temporal alignements: Optimal transport in space and time
  ☆49Updated 9 months ago
• tiskw / random-fourier-features
  Implementation of random Fourier features for kernel method, like support vector machine and Gaussian process model
  ☆103Updated last year
• ebekkers / gsplinets
  B-Spline CNNs on Lie groups
  ☆57Updated 6 years ago
• MachineLearningLifeScience / stochman
  Algorithms for computations on random manifolds made easier
  ☆94Updated 2 years ago
• ParthaEth / Regularized_autoencoders-RAE-
  ☆125Updated 3 years ago
• amirgholami / anode
  [IJCAI'19, NeurIPS'19] Anode: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
  ☆109Updated 5 years ago
• TeaPearce / Bayesian_NN_Ensembles
  AISTATS paper 'Uncertainty in Neural Networks: Approximately Bayesian Ensembling'
  ☆90Updated 5 years ago
• fmeirinhos / pytorch-hessianfree
  PyTorch implementation of Hessian Free optimisation
  ☆43Updated 6 years ago
• judelo / gmmot
  Python notebooks for Optimal Transport between Gaussian Mixture Models
  ☆47Updated 4 years ago
• ratschlab / bnn_priors
  Code for the paper "Bayesian Neural Network Priors Revisited"
  ☆60Updated 4 years ago
• cunningham-lab / cb_and_cc
  ☆37Updated 3 years ago
• brain-research / nngp
  Deep neural network kernel for Gaussian process
  ☆212Updated 5 years ago