Alternatives and similar repositories for Non-Convex-Newton

Users that are interested in Non-Convex-Newton are comparing it to the libraries listed below

• Bihaqo / t3f
  Tensor Train decomposition on TensorFlow
  ☆228Updated 4 years ago
• hiroyuki-kasai / RSOpt
  Riemannian stochastic optimization algorithms: Version 1.0.3
  ☆67Updated 2 years ago
• gowerrobert / StochOpt.jl
  A suite of stochastic optimization methods for solving the empirical risk minimization problem.
  ☆17Updated 6 years ago
• IssamLaradji / BlockCoordinateDescent
  Code for the paper "Let’s Make Block Coordinate Descent Go Fast"
  ☆48Updated 2 years ago
• google / wasserstein-dist
  tensorflow implementation of the Wasserstein (aka optimal transport) distance
  ☆73Updated 4 years ago
• ucla-vision / entropy-sgd
  Lua implementation of Entropy-SGD
  ☆81Updated 7 years ago
• dalab / subsampled_cubic_regularization
  Source code for "Sub-sampled Cubic Regularization for Non-convex Optimization", JM Kohler, A Lucchi, https://arxiv.org/abs/1705.05933
  ☆16Updated 7 years ago
• jmdvinodjmd / LIBS2ML
  LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms
  ☆12Updated 4 years ago
• ymalitsky / adaptive_GD
  Adaptive gradient descent without descent
  ☆50Updated 4 years ago
• Simon-Lacoste-Julien / linearFW
  Frank-Wolfe optimization variants with a linear convergence rate
  ☆22Updated 9 years ago
• aditya-grover / uae
  Uncertainty Autoencoders, AISTATS 2019
  ☆56Updated 6 years ago
• martinResearch / PySparseLP
  python algorithms to solve sparse linear programming problems
  ☆33Updated 2 years ago
• gpeyre / 2021-NonCvxPro
  ☆11Updated 4 years ago
• mingzhang-yin / SIVI
  A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural netwo…
  ☆54Updated last year
• ermongroup / sparse_gen
  Code for "Modeling Sparse Deviations for Compressed Sensing using Generative Models", ICML 2018
  ☆24Updated 7 years ago
• maremun / quffka
  Quadrature-based features for kernel approximation
  ☆16Updated 7 years ago
• izmailovpavel / TTGP
  ☆26Updated 7 years ago
• keskarnitish / minSQN
  Optimization using Stochastic quasi-Newton methods
  ☆42Updated 8 years ago
• maka89 / Deep-Kernel-GP
  Deep Kernel Learning. Gaussian Process Regression where the input is a neural network mapping of x that maximizes the marginal likelihood
  ☆95Updated 8 years ago
• mctorch / mctorch
  A manifold optimization library for deep learning
  ☆250Updated 4 years ago
• widedeepnetworks / widedeepnetworks
  Code for the paper Gaussian process behaviour in wide deep networks
  ☆46Updated 7 years ago
• mariogeiger / hessian
  hessian in pytorch
  ☆187Updated 5 years ago
• lezcano / expRNN
  Optimization with orthogonal constraints and on general manifolds
  ☆130Updated 5 years ago
• wiseodd / natural-gradients
  Collection of algorithms for approximating Fisher Information Matrix for Natural Gradient (and second order method in general)
  ☆142Updated 6 years ago
• fKunstner / limitations-empirical-fisher
  Limitations of the Empirical Fisher Approximation
  ☆49Updated 9 months ago
• rajatvd / NeuralODE
  Experiments with Neural ODEs and Adversarial Attacks
  ☆44Updated 6 years ago
• mstaib / stochastic-barycenter-code
  Supporting code for "Parallel Streaming Wasserstein Barycenters"
  ☆10Updated 8 years ago
• gaoliyao / Replica_Exchange_Stochastic_Gradient_MCMC
  Code for Non-convex Learning via Replica Exchange Stochastic Gradient MCMC, ICML 2020.
  ☆26Updated 5 years ago
• kirthevasank / nasbot
  Neural Architecture Search with Bayesian Optimisation and Optimal Transport
  ☆135Updated 6 years ago
• amirgholami / HessianFlow
  ☆83Updated 5 years ago