Machine learning reveals hidden landscape of robust information storage

In a new study published in Physical Review Letters, researchers used machine learning to discover multiple new classes of ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
Frontiers

A computational approach for prediction of exons using static encoding methods, digital filter and windowing technique

School of Electronics Engineering, Vellore Institute of Technology, Vellore, India Introduction: Identifying protein-coding regions in eukaryotic Deoxyribonucleic acid (DNA) remains difficult due to ...
Frontiers

Comparison of multiple machine learning methods for predicting postoperative hyperglycemia in patients without diabetes undergoing cardiac surgery

Background: Stress-induced hyperglycemia (SHG) represents a significant metabolic complication in non-diabetic cardiac surgery older adult patients, with substantial implications for postoperative ...
Scientific Research Publishing

Wilson, A.C., Roelofs, R., Stern, M., Srebro, N. and Recht, B. (2017) The Marginal Value of Adaptive Gradient Methods in Machine Learning. Advances in Neural Information ...

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
National Academies of Sciences%2c Engineering%2c and Medicine

Making Machine Learning Safer in High-Stakes Settings

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...
PNAS

Machine learning in chemistry

The Perspective by Tiwary et al. (8) offers a comprehensive overview of generative AI methods in computational chemistry. Approaches that generate new outputs (e.g., inferring phase transitions) by ...
IEEE

Feature Construction Using Network Control Theory and Rank Encoding for Graph Machine Learning

Abstract: In this article, we utilize the concept of average controllability in graphs, along with a novel rank encoding method, to enhance the performance of Graph Neural Networks (GNNs) in social ...
Scientific Research Publishing

Carvalho, D. V., Pereira, E. M., & Cardoso, J. S. (2019). Machine Learning Interpretability: A Survey on Methods and Metrics. Electronics, 8, Article 832.

ABSTRACT: Machine learning (ML) has revolutionized risk management by enabling organizations to make data-driven decisions with higher accuracy and speed. However, as machine learning models grow more ...
chromatographyonline

Method Robustness, Retention Alignment, and Machine Learning in 2D LC: An HPLC 2025 Video Interview with Bob Pirok

LCGC International interviewed Bob Pirok from the University of Amsterdam, Netherlands to discuss strategies for enhancing method robustness in 2D LC, practical approaches for tracking peaks across ...