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Deep variational autoencoder for proteomics mass spectrometry data analysis

Jianwei Shuai's team and Jiahuai Han's team at Xiamen University have developed a deep autoencoder-based data-independent acquisition data analysis software for protein mass spectrometry, which ...
IEEE

Cluster-Memory Augmented Deep Autoencoder via Optimal Transportation for Hyperspectral Anomaly Detection

Abstract: Hyperspectral anomaly detection (AD) aims to detect objects significantly different from their surrounding background. Recently, many detectors based on autoencoder (AE) exhibited promising ...
IEEE

Topological Attention Graph Neural ODE Deep Clustering for UAV Swarms in Encirclement Attack Scenarios

Abstract: In encirclement attacks scenarios, hostile Unmanned Aerial Vehicles (UAVs) swarms commonly employ coordinated tactics of decoy and strike formations. This tendency poses significant ...
GitHub

FAEclust: Cluster Analysis of Multi-Dimensional Functional Data

FAEclust is the first autoencoder architecture designed specifically for clustering multi-dimensional functional data. In FAEclust, we employ univariate functions as weights instead of integral ...
International Monetary Fund

UnFEAR: Unsupervised Feature Extraction Clustering with an Application to Crisis Regimes Classification

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the ...
GitHub

Hierarchical Risk Parity with Autoencoder-based Covariance Estimation and Deep Neural Network Sector Rotation in the Johannesburg Stock Exchange

The project integrates Autoencoder-based covariance estimation, Hierarchical Risk Parity (HRP) allocation, and Deep Neural Network (DNN)-driven sector rotation to improve portfolio diversification and ...