PyHGF: A neural network library for predictive coding
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Updated
Jun 27, 2024 - Python
PyHGF: A neural network library for predictive coding
Official PyTorch implementation of the CVPR 2024 paper: State Space Models for Event Cameras (Spotlight).
Official Implementation of the work "Audio Mamba: Bidirectional State Space Model for Audio Representation Learning"
[IEEE TGRS 2024] ChangeMamba: Remote Sensing Change Detection Based on Spatio-Temporal State Space Model
A PyTorch implementation of the paper "ZigMa: A DiT-Style Mamba-based Diffusion Model"
Accelerated First Order Parallel Associative Scan
Statecraft - Load, store and remix states for SSMs, Mamba and Stateful models
PointMamba: A Simple State Space Model for Point Cloud Analysis
Official implementation of our paper "Scalable Event-by-event Processing of Neuromorphic Sensory Signals With Deep State-Space Models"
An official implementation for SSAMBA: Self-Supervised Audio Mamba
We use EM for a mixture of state space models to perform unsupervised clustering of short trajectories.
Arbitrage-free Dynamic Generalized Nelson-Siegel model of interest rates following Christensen, Diebold and Rudebusch; and its estimation using the Kalman filter / maximum likelihood.
Spectral State-Space Models
Variational Joint Filtering
Neural State-Space Models and Latent Dynamics Functions in PyTorch for High-Dimensional Forecasting
Implementation of different Lorenz models (Matlab and Python)
Factorial latent dynamic models trained on Markovian simulations of biological processes using single cell RNA sequencing data.
Simple implementations of long-range sequence models (LRU, S5, S4, and more).
Imputation-based Time-Series Anomaly Detection with Conditional Weight-Incremental Diffusion Models, KDD 2023
PyTorch implementation of the NCDSSM models presented in the ICML '23 paper "Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series".
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