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
Advanced POD-Based Performance Evaluation of Classifiers Applied to Human Driver Lane Changing Prediction
Zahra Rastin, Dirk Söffker
Ionospheric Scintillation Forecasting Using Machine Learning
Sultan Halawa, Maryam Alansaari, Maryam Sharif, Amel Alhammadi, Ilias Fernini
Machine Learning of Nonlinear Dynamical Systems with Control Parameters Using Feedforward Neural Networks
Hidetsugu Sakaguchi
What Machine Learning Tells Us About the Mathematical Structure of Concepts
Jun Otsuka
A Statistical Framework for Data-dependent Retrieval-Augmented Models
Soumya Basu, Ankit Singh Rawat, Manzil Zaheer
Evaluating Pre-Training Bias on Severe Acute Respiratory Syndrome Dataset
Diego Dimer Rodrigues
Latent Ewald summation for machine learning of long-range interactions
Bingqing Cheng
Evaluating the Energy Consumption of Machine Learning: Systematic Literature Review and Experiments
Charlotte Rodriguez, Laura Degioanni, Laetitia Kameni, Richard Vidal, Giovanni Neglia
Machine Learning for Methane Detection and Quantification from Space - A survey
Enno Tiemann, Shanyu Zhou, Alexander Kläser, Konrad Heidler, Rochelle Schneider, Xiao Xiang Zhu
Deep learning classification system for coconut maturity levels based on acoustic signals
June Anne Caladcad, Eduardo Jr Piedad
Adaptive Resolution Inference (ARI): Energy-Efficient Machine Learning for Internet of Things
Ziheng Wang, Pedro Reviriego, Farzad Niknia, Javier Conde, Shanshan Liu, Fabrizio Lombardi
Assessing Contamination in Large Language Models: Introducing the LogProber method
Nicolas Yax, Pierre-Yves Oudeyer, Stefano Palminteri
Machine Learning for Quantifier Selection in cvc5
Jan Jakubův, Mikoláš Janota, Jelle Piepenbrock, Josef Urban
Automated Machine Learning in Insurance
Panyi Dong, Zhiyu Quan
Streamline tractography of the fetal brain in utero with machine learning
Weide Liu, Camilo Calixto, Simon K. Warfield, Davood Karimi
Category-Theoretical and Topos-Theoretical Frameworks in Machine Learning: A Survey
Yiyang Jia, Guohong Peng, Zheng Yang, Tianhao Chen
Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Review
Shaina Raza, Arash Shaban-Nejad, Elham Dolatabadi, Hiroshi Mamiya
A Web-Based Solution for Federated Learning with LLM-Based Automation
Chamith Mawela, Chaouki Ben Issaid, Mehdi Bennis