Alternatives and similar repositories for ode-lstms:

Users that are interested in ode-lstms are comparing it to the libraries listed below

• ratschlab / GP-VAE
  TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155
  ☆130Updated last year
• edebrouwer / gru_ode_bayes
  Pytorch implementation of GRU-ODE-Bayes
  ☆227Updated 2 years ago
• boschresearch / Continuous-Recurrent-Units
  ☆60Updated last year
• facebookresearch / neural_stpp
  Deep generative modeling for time-stamped heterogeneous data, enabling high-fidelity models for a large variety of spatio-temporal domain…
  ☆101Updated 3 years ago
• wassname / attentive-neural-processes
  implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)
  ☆91Updated 10 months ago
• crisbodnar / ndp
  Official code for the ICLR 2021 paper Neural ODE Processes
  ☆70Updated 2 years ago
• jambo6 / online-neural-cdes
  Code for: "Neural Controlled Differential Equations for Online Prediction Tasks"
  ☆37Updated 2 years ago
• cagatayyildiz / ODE2VAE
  ODE2VAE: Deep generative second order ODEs with Bayesian neural networks
  ☆125Updated 4 months ago
• 000Justin000 / torchdiffeq
  Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
  ☆58Updated 3 years ago
• patrick-kidger / torchcde
  Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.
  ☆423Updated last year
• jambo6 / neuralRDEs
  Code for: "Neural Rough Differential Equations for Long Time Series", (ICML 2021)
  ☆115Updated 3 years ago
• john-x-jiang / meta_ssm
  Repository for ICLR 2023 work, "Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting"
  ☆29Updated 4 months ago
• mlds-lab / interp-net
  Code for "Interpolation-Prediction Networks for Irregularly Sampled Time Series", ICLR 2019.
  ☆95Updated 5 months ago
• steveli / partial-encoder-decoder
  An encoder-decoder framework for learning from incomplete data
  ☆46Updated last year
• mbilos / neural-flows-experiments
  Experiments for Neural Flows paper
  ☆92Updated 3 years ago
• YuliaRubanova / latent_ode
  Code for "Latent ODEs for Irregularly-Sampled Time Series" paper
  ☆535Updated 4 years ago
• BorgwardtLab / Set_Functions_for_Time_Series
  Repository of the ICML 2020 paper "Set Functions for Time Series"
  ☆122Updated 3 years ago
• helange23 / from_fourier_to_koopman
  Linear and non-linear spectral forecasting algorithms
  ☆135Updated 3 years ago
• abdulfatir / REDSDS
  Pytorch implementation of RED-SDS (NeurIPS 2021).
  ☆18Updated 3 years ago
• patrick-kidger / generalised_shapelets
  Code for "Generalised Interpretable Shapelets for Irregular Time Series"
  ☆56Updated last year
• vincent-leguen / STRIPE
  Code for our NeurIPS 2020 paper "Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity"
  ☆87Updated 3 years ago
• mbohlkeschneider / gluon-ts
  GluonTS - Probabilistic Time Series Modeling in Python
  ☆51Updated 3 years ago
• loeweX / AmortizedCausalDiscovery
  Code for the paper: Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data
  ☆206Updated 2 years ago
• XAI-360 / TSCL
  Contrastive Learning for Time Series
  ☆38Updated last year
• edebrouwer / latentCCM
  Implementation of the Latent CCM paper
  ☆14Updated 7 months ago
• erichson / koopmanAE
  Consistent Koopman Autoencoders
  ☆67Updated last year
• kamilest / conformal-rnn
  Implementation for Stankevičiūtė et al. "Conformal time-series forecasting", NeurIPS 2021.
  ☆69Updated 2 months ago
• felixykliu / NAOMI
  ☆51Updated 5 years ago
• patrick-kidger / NeuralCDE
  Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
  ☆633Updated 2 years ago
• networkslab / rnn_flow
  ☆22Updated 3 years ago