Structured state space sequence models

Code Repository for Liquid Time-Constant Networks (LTCs)

State Space Models library in JAX

Liquid Structural State-Space Models

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

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

Reading list for research topics in state-space models

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

Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. This includes models like Mambular, FT-Transformer, TabTransformer and tabular ResNets.

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

Package implementing common state-space routines.

Official PyTorch implementation of the CVPR 2024 paper: State Space Models for Event Cameras (Spotlight).

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

Multivariate Autoregressive State-Space Modeling with R

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

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

fit latent variable movement models to animal tracking data

List of papers related to State Space Models (Mamba) in Vision.