IEEE

Flexible Background Modeling for Hyperspectral Anomaly Detection Using Convolutional Sparse Coding

Abstract: This paper proposes a hyperspectral (HS) anomaly detection method using a novel background model based on convolutional sparse coding (CSC). One promising HS anomaly detection approach is to ...
GitHub

Pearl - A Production-ready Reinforcement Learning AI Agent Library

Pearl is a new production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta. Furthering our efforts on open AI innovation, Pearl enables ...
Frontiers

Sparse Gaussian Process Regression for Landslide Displacement Time-Series Forecasting

Landslide hazards are complex nonlinear systems with a highly dynamic nature. Accurate forecasting of landslide displacement and evolution is crucial for the prevention and mitigation of landslide ...
C&EN

High-Resolution Hydrogen–Deuterium Protection Factors from Sparse Mass Spectrometry Data Validated by Nuclear Magnetic Resonance Measurements

School of Molecular and Cellular Biology, University of Leeds, LS2 9JT Leeds, United Kingdom ...
GitHub

Sparse InversE Covariance estimation for Ecological Association and Statistical Inference

This package will be useful to anybody who wants to infer graphical models for all sorts of compositional data, though primarily intended for microbiome relative abundance data (generated from 16S ...
Frontiers

Machine learning for neuroimaging with scikit-learn

Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g., multivariate analysis of ...