Machine Learning
Machine learning (ML) focuses on developing algorithms that allow computers to learn from data without explicit programming, aiming to improve prediction accuracy, automate tasks, and extract insights. Current research emphasizes areas like fairness in federated learning, efficient model training and deployment (including techniques to reduce communication overhead), and enhancing model interpretability and robustness against adversarial attacks. ML's impact spans diverse fields, from healthcare (e.g., disease prediction) and industrial quality control to astrophysics (e.g., galaxy classification) and cybersecurity, demonstrating its broad applicability and significant potential for scientific advancement and practical problem-solving.
Papers
Accelerating Giant Impact Simulations with Machine Learning
Caleb Lammers, Miles Cranmer, Sam Hadden, Shirley Ho, Norman Murray, Daniel Tamayo
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Raphael T. Husistein, Markus Reiher, Marco Eckhoff
ML Study of MaliciousTransactions in Ethereum
Natan Katz
A Novel Fusion of Optical and Radar Satellite Data for Crop Phenology Estimation using Machine Learning and Cloud Computing
Shahab Aldin Shojaeezadeh, Abdelrazek Elnashar, Tobias Karl David Weber
Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Review of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems
Wang-Ji Yan, Lin-Feng Mei, Jiang Mo, Costas Papadimitriou, Ka-Veng Yuen, Michael Beer
Machine learning empowered Modulation detection for OFDM-based signals
Ali Pourranjbar, Georges Kaddoum, Verdier Assoume Mba, Sahil Garg, Satinder Singh
Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through Simple Vocal Signals, Transforming any Speech into a Sensor with Machine Learning
Nihat Ahmadli, Mehmet Ali Sarsil, Onur Ergen
Graph representations of 3D data for machine learning
Tomasz Prytuła
Capturing the Complexity of Human Strategic Decision-Making with Machine Learning
Jian-Qiao Zhu, Joshua C. Peterson, Benjamin Enke, Thomas L. Griffiths
A Comprehensive Case Study on the Performance of Machine Learning Methods on the Classification of Solar Panel Electroluminescence Images
Xinyi Song, Kennedy Odongo, Francis G. Pascual, Yili Hong
A Large-Scale Study of Model Integration in ML-Enabled Software Systems
Yorick Sens, Henriette Knopp, Sven Peldszus, Thorsten Berger
Multimodal Large Language Models for Phishing Webpage Detection and Identification
Jehyun Lee, Peiyuan Lim, Bryan Hooi, Dinil Mon Divakaran
Low-Rank Approximation, Adaptation, and Other Tales
Jun Lu