state-space-models

Structured state space sequence models

Code Repository for Liquid Time-Constant Networks (LTCs)

A Python package for probabilistic state space modeling with JAX

Liquid Structural State-Space Models

Reading list for research topics in state-space models

StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.

Mambular is a Python package that simplifies tabular deep learning by providing a suite of models for regression, classification, and distributional regression tasks. It includes models such as Mambular, TabM, FT-Transformer, TabulaRNN, TabTransformer, and tabular ResNets.

Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.

R code for Time Series Analysis and Its Applications, Ed 4

Julia package for automatically generating Bayesian inference algorithms through message passing on Forney-style factor graphs.

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

[CVPR'24 Spotlight] The official implementation of "State Space Models for Event Cameras"

[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining

Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX

Package implementing common state-space routines.

The official codebase of the paper "Chemical language modeling with structured state space sequence models"

Recall to Imagine, a model-based RL algorithm with superhuman memory. Oral (1.2%) @ ICLR 2024

Multivariate Autoregressive State-Space Modeling with R

fit latent variable movement models to animal tracking data

Official implementation of our ECCV paper "StretchBEV: Stretching Future Instance Prediction Spatially and Temporally"