Alternatives and similar repositories for isomap:

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

• maziarraissi / ParametricGP
  Parametric Gaussian Process Regression for Big Data
  ☆44Updated 5 years ago
• ngorbach / Variational_Gradient_Matching_for_Dynamical_Systems
  Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"
  ☆26Updated 5 years ago
• josiahw / IsomapCUDA
  a python powered CUDA isomap implementation.
  ☆12Updated 11 years ago
• sremes / nssm-gp
  Non-stationary spectral mixture kernels implemented in GPflow
  ☆28Updated 6 years ago
• Sohl-Dickstein / Hamiltonian-Annealed-Importance-Sampling
  Matlab code implementing Hamiltonian Annealed Importance Sampling for importance weight, partition function, and log likelihood estimatio…
  ☆26Updated 10 years ago
• gianlucadetommaso / Stein-variational-samplers
  Python and MATLAB code for Stein Variational sampling methods
  ☆24Updated 5 years ago
• karlnapf / kernel_hmc
  Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"
  ☆25Updated 6 years ago
• xinshi-chen / ParticleFlowBayesRule
  Pytorch implementation for "Particle Flow Bayes' Rule"
  ☆14Updated 5 years ago
• danieljtait / ladax
  Layered distributions using FLAX/JAX
  ☆10Updated 4 years ago
• hegdepashupati / differential-dgp
  Implementation of stochastic variational inference for differentially deep gaussian processes
  ☆15Updated 6 years ago
• ZichaoLong / aTEAM
  A pyTorch Extension for Applied Mathematics
  ☆39Updated 4 years ago
• kd383 / GPML_SLD
  Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)
  ☆18Updated 7 years ago
• thangbui / geepee
  A collection of Gaussian process models
  ☆30Updated 7 years ago
• thangbui / deepGP_approxEP
  see https://github.com/thangbui/geepee for a faster implementation
  ☆37Updated 7 years ago
• LCSL / NystromCoRe
  Rudi, A., Camoriano, R. and Rosasco, L., Less is more: Nyström computational regularization. In Advances in Neural Information Processing…
  ☆12Updated 5 years ago
• davmre / gprf
  Gaussian Process Random Fields
  ☆21Updated 9 years ago
• sremes / nonstationary-spectral-kernels
  Implementation for Non-stationary Spectral Kernels (NIPS 2017)
  ☆20Updated 5 years ago
• n-takeishi / bayesiandmd
  Bayesian Dynamic Mode Decomposition (Bayesian DMD)
  ☆18Updated 3 years ago
• bwohlberg / sporco-cuda
  CUDA extension for the SPORCO project
  ☆18Updated 3 years ago
• YingzhenLi / Dropout_BBalpha
  Implementations of the ICML 2017 paper (with Yarin Gal)
  ☆38Updated 7 years ago
• wzhi / ODElearning_INN
  [ICML 2022] Learning Efficient and Robust Ordinary Differential \\ Equations via Invertible Neural Networks
  ☆10Updated last year
• mijungi / lllvm
  ☆16Updated 9 years ago
• convnets-as-gps / convnets-as-gps
  Code for "Deep Convolutional Networks as shallow Gaussian Processes"
  ☆16Updated 5 years ago
• AaltoML / hilbert-gp
  Codes for Hilbert space reduced-rank GP regression
  ☆14Updated 5 years ago
• wjmaddox / spectralgp
  Code repo for "Function-Space Distributions over Kernels"
  ☆31Updated 4 years ago
• karlnapf / kameleon-mcmc
  Code for Kernel Adaptive Metropolis-Hastings
  ☆33Updated 10 years ago
• gpeyre / 2015-SIIMS-wasserstein-jko
  Gabriel Peyré. Entropic Approximation of Wasserstein Gradient Flows. SIAM Journal on Imaging Sciences, 8(4), pp. 2323–2351, 2015
  ☆17Updated 8 years ago
• Sohl-Dickstein / LAHMC
  Look Ahead Hamiltonian Monte Carlo
  ☆30Updated 9 years ago
• n-takeishi / learning-kis
  Demo implementation of Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition
  ☆40Updated 3 years ago
• PredictiveIntelligenceLab / JAX-BO
  ☆30Updated 2 years ago