Newton-based maximum likelihood estimation in nonlinear state space models

Conrol theory project a friend and I did for our Intelligent Control class during our minor (undergraduate level)

Improve the Attentive State-Space Model by transformer

Control with sympy

State space models for categorization of replay content from multiunit spiking activity. Deng et al. 2016

Sound source localization by locally fitting autonomous state space models (LSSMs)

Python state-space models

A flexible data simulator for Kafka and OpenShift using state-space models

Repository for the 2022 MLMI work, "Neural State-Space Modeling with Latent Causal-Effect Disentanglement."

Feed Hungry Hungry Hippos (H3) - Do Languange Modeling with a 🦛 (unofficial)

This repository contains the source code for "Stochastic data-driven model predictive control using Gaussian processes" (SDD-GP-MPC).

PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series".

Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models, KDD 2023

Simple implementations of long-range sequence models (LRU, S5, S4, and more).

Implementation of different Lorenz models (Matlab and Python)

Neural State-Space Models and Latent Dynamics Functions in PyTorch for High-Dimensional Forecasting

Variational Joint Filtering

Spectral State-Space Models

Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.

We use EM for a mixture of state space models to perform unsupervised clustering of short trajectories.