Frontiers

Deep Learning for Event Reconstruction in Neutrino Experiments

Neutrino experiments are entering an era of unprecedented detector scale, precision, and data complexity. From liquid argon time projection chambers and ...
Nature

A unified deep learning application framework for forensic shoeprint analysis under data-limited conditions

This work was supported by the National Research Foundation of Korea (NRF) under Grant No. 2021R1C1C100711111, and by the Basic Science Research Program through the National Research Foundation of ...
IEEE

TLSCG: Transfer Learning-Based Smart Contract Generation to Empower Unknown Vulnerability Detection in Blockchain Services

Abstract: Blockchains increasingly enable decentralized and trustworthy service execution. Smart contracts, i.e., self-executing programs on the blockchain, are essential for automating services. Yet, ...
IEEE

Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms

Abstract: The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As ...
GitHub

Blind Source Separation of Single-Channel Mixtures via Multi-Encoder Autoencoders

As the foundation of our proposed method, we use multi-encoder autoencoders such that each encoder receives the same input, and the outputs of each encoder are concatenated along the channel dimension ...
GitHub

Graph Neural Networks for Tabular Data Learning (GNN4TDL)

The deep learning-based approaches to Tabular Data Learning (TDL), classification and regression, have shown competing performance, compared to their conventional counterparts. However, the latent ...
ascopubs.org

A transfer-learned hierarchical variational autoencoder model for computational design of anticancer peptides.

Recent advances in feature selection methods for breast cancer recurrence prediction: A systematic review. This is an ASCO Meeting Abstract from the 2025 ASCO Annual Meeting I. This abstract does not ...
Nature

Restoration of multi-channel signal loss using autoencoder with recursive input strategy

Multi-channel sensor data often suffer from missing or corrupted values due to sensor failures, communication disruptions, or environmental interference. These issues severely limit the accuracy of ...
mccormick.northwestern.edu

Andrew Crotty Receives Google Research Scholar Program Award

In the big data era, algorithms for efficient data compression have become increasingly crucial, both for transmission over the network and long-term archival. Most traditional compression techniques, ...
Visual Studio Magazine

Data Anomaly Detection Using a Neural Autoencoder with C#

Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
C&EN

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes

Department of Chemistry, Theoretical Chemistry Institute, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States Department of Chemistry, Theoretical Chemistry Institute, University ...