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
NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design
Mouadh Yagoubi, David Danan, Milad Leyli-abadi, Jean-Patrick Brunet, Jocelyn Ahmed Mazari, Florent Bonnet, maroua gmati, Asma Farjallah, Paola Cinnella, Patrick Gallinari, Marc Schoenauer
A Linear Programming Enhanced Genetic Algorithm for Hyperparameter Tuning in Machine Learning
Ankur Sinha, Paritosh Pankaj
ML Updates for OpenStreetMap: Analysis of Research Gaps and Future Directions
Lasith Niroshan, James D. Carswell
Machine Learning Predictors for Min-Entropy Estimation
Javier Blanco-Romero, Vicente Lorenzo, Florina Almenares Mendoza, Daniel Díaz-Sánchez
Systematic Literature Review on Application of Learning-based Approaches in Continuous Integration
Ali Kazemi Arani, Triet Huynh Minh Le, Mansooreh Zahedi, M. Ali Babar
A Survey on Data Quality Dimensions and Tools for Machine Learning
Yuhan Zhou, Fengjiao Tu, Kewei Sha, Junhua Ding, Haihua Chen
Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques
Abraham G Taye, Sador Yemane, Eshetu Negash, Yared Minwuyelet, Moges Abebe, Melkamu Hunegnaw Asmare
Generating Is Believing: Membership Inference Attacks against Retrieval-Augmented Generation
Yuying Li, Gaoyang Liu, Chen Wang, Yang Yang
Machine learning meets mass spectrometry: a focused perspective
Daniil A. Boiko, Valentine P. Ananikov
Predicting the duration of traffic incidents for Sydney greater metropolitan area using machine learning methods
Artur Grigorev, Sajjad Shafiei, Hanna Grzybowska, Adriana-Simona Mihaita
Probing the effects of broken symmetries in machine learning
Marcel F. Langer, Sergey N. Pozdnyakov, Michele Ceriotti
Generalizability of experimental studies
Federico Matteucci, Vadim Arzamasov, Jose Cribeiro-Ramallo, Marco Heyden, Konstantin Ntounas, Klemens Böhm
Stacked Confusion Reject Plots (SCORE)
Stephan Hasler, Lydia Fischer
Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning
Chun Wang, Jiexiao Chen, Ziyang Xie, Jianke Zou
A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design
Yossra Gharbi, Rocío Mercado
SyROCCo: Enhancing Systematic Reviews using Machine Learning
Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier, Juliana Outes Velarde, Harry Bregazzi, Mara Airoldi, Eleanor Carter, Rob Procter