Alternatives and similar repositories for signatory

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

• patrick-kidger / Deep-Signature-Transforms
  Code for "Deep Signature Transforms" (NeurIPS 2019)
  ☆97Updated last year
• bottler / iisignature
  Iterated integral signature calculations
  ☆114Updated last month
• anh-tong / signax
  Signax: Signature computation in JAX
  ☆37Updated 10 months ago
• kormilitzin / the-signature-method-in-machine-learning
  Foundations and Applications
  ☆101Updated 5 years ago
• datasig-ac-uk / esig
  esig python package
  ☆50Updated 11 months ago
• tgcsaba / GPSig
  Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances
  ☆50Updated 2 years ago
• patrick-kidger / torchcde
  Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
  ☆461Updated 3 months ago
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆121Updated 4 years ago
• jambo6 / generalised-signature-method
  Code for: "A Generalised Signature Method for Time Series"
  ☆65Updated 2 years ago
• wesselb / stheno
  Gaussian process modelling in Python
  ☆225Updated 11 months ago
• TyXe-BDL / TyXe
  ☆155Updated 3 years ago
• automl / TransformersCanDoBayesianInference
  Official Implementation of "Transformers Can Do Bayesian Inference", the PFN paper
  ☆240Updated last year
• freelunchtheorem / Conditional_Density_Estimation
  Package implementing various parametric and nonparametric methods for conditional density estimation
  ☆197Updated 2 years ago
• crispitagorico / sigkernel
  Differentiable computations for the signature-PDE-kernel on CPU and GPU.
  ☆56Updated last month
• patrick-kidger / NeuralCDE
  Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
  ☆689Updated 3 years ago
• secondmind-labs / GPflux
  Deep GPs built on top of TensorFlow/Keras and GPflow
  ☆127Updated last year
• YannDubs / Neural-Process-Family
  Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.
  ☆227Updated last year
• probml / sts-jax
  Structural Time Series in JAX
  ☆210Updated last year
• xwinxu / bayeSDE
  Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"
  ☆171Updated 3 years ago
• AdamCobb / hamiltorch
  PyTorch-based library for Riemannian Manifold Hamiltonian Monte Carlo (RMHMC) and inference in Bayesian neural networks
  ☆465Updated last year
• datasig-ac-uk / signature_applications
  Example applications of path signatures
  ☆41Updated 8 months ago
• facebookincubator / flowtorch
  This library would form a permanent home for reusable components for deep probabilistic programming. The library would form and harness a…
  ☆311Updated 5 months ago
• amzn / orthogonal-additive-gaussian-processes
  Light-weighted code for Orthogonal Additive Gaussian Processes
  ☆43Updated last year
• pafoster / path_signatures_introduction
  A Brief Introduction to Path Signatures for Machine Learning Practitioners
  ☆55Updated 4 years ago
• FalkonML / falkon
  Large-scale, multi-GPU capable, kernel solver
  ☆192Updated 4 months ago
• g-benton / Volt
  Public Implementation of Volatility Based Kernels and Moving Average Means for Accurate Forecasting with Gaussian Processes
  ☆49Updated 3 years ago
• jeremiecoullon / SGMCMCJax
  Lightweight library of stochastic gradient MCMC algorithms written in JAX.
  ☆105Updated 2 years ago
• AaltoML / BayesNewton
  Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
  ☆241Updated last year
• jacobjinkelly / easy-neural-ode
  Code for the paper "Learning Differential Equations that are Easy to Solve"
  ☆286Updated 4 years ago
• johannbrehmer / manifold-flow
  Manifold-learning flows (ℳ-flows)
  ☆231Updated 5 years ago