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
Modeling offensive content detection for TikTok
Kasper Cools, Gideon Mailette de Buy Wenniger, Clara Maathuis
Automatic detection of Mild Cognitive Impairment using high-dimensional acoustic features in spontaneous speech
Cong Zhang, Wenxing Guo, Hongsheng Dai
A Catalog of Fairness-Aware Practices in Machine Learning Engineering
Gianmario Voria, Giulia Sellitto, Carmine Ferrara, Francesco Abate, Andrea De Lucia, Filomena Ferrucci, Gemma Catolino, Fabio Palomba
Optimizing Automated Picking Systems in Warehouse Robots Using Machine Learning
Keqin Li, Jin Wang, Xubo Wu, Xirui Peng, Runmian Chang, Xiaoyu Deng, Yiwen Kang, Yue Yang, Fanghao Ni, Bo Hong
The Application of Machine Learning in Tidal Evolution Simulation of Star-Planet Systems
Shuaishuai Guo, Jianheng Guo, KaiFan Ji, Hui Liu, Lei Xing
Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning
Huili Zheng, Qimin Zhang, Yiru Gong, Zheyan Liu, Shaohan Chen
Fairness, Accuracy, and Unreliable Data
Kevin Stangl
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