Structured state space sequence models
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Updated
Jul 17, 2024 - Jupyter Notebook
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.
Reading list for research topics in 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.
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.
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
Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
[CVPR'24 Spotlight] The official implementation of "State Space Models for Event Cameras"
Package implementing common state-space routines.
[ACM MM'24 Oral] RainMamba: Enhanced Locality Learning with State Space Models for Video Deraining
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
Official implementation of our ECCV paper "StretchBEV: Stretching Future Instance Prediction Spatially and Temporally"
List of papers related to State Space Models (Mamba) in Vision.
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