Code Repository for Liquid Time-Constant Networks (LTCs)
-
Updated
Aug 21, 2023 - Python
Code Repository for Liquid Time-Constant Networks (LTCs)
Liquid Structural 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.
Recall 2 Imagine, a World Model with superhuman memory. Oral (1.2%) @ ICLR 2024
Official implementation of our ECCV paper "StretchBEV: Stretching Future Instance Prediction Spatially and Temporally"
Official PyTorch implementation of the CVPR 2024 paper: State Space Models for Event Cameras.
Gradient-informed particle MCMC methods
Official implementation of the CBF-SSM model
Variational Filtering via Wasserstein Gradient Flow
Simulates the dynamics of a Reaction Wheel Inverted Pendulum with python.
A Python package to demonstrate ideas from nonlinear dynamical systems toward game theory, neural network models of associative memory, and nonlinear state space models.
Second-order iterated smoothing algorithms for state estimation
Switching linear dynamical systems (SLDS) models in JAX
Correlated pseudo-marginal Metropolis-Hastings using quasi-Newton proposals
Quasi-Newton particle Metropolis-Hastings
Code supplement for "Unsupervised multimodal modeling of cognitive and brain health trajectories for early dementia prediction"
Code for the paper "Backward importance sampling for online estimation of state space models"
Add a description, image, and links to the state-space-models topic page so that developers can more easily learn about it.
To associate your repository with the state-space-models topic, visit your repo's landing page and select "manage topics."