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
Can machine learning solve the challenge of adaptive learning and the individualization of learning paths? A field experiment in an online learning platform
Tim Klausmann, Marius Köppel, Daniel Schunk, Isabell Zipperle
How Reliable and Stable are Explanations of XAI Methods?
José Ribeiro, Lucas Cardoso, Vitor Santos, Eduardo Carvalho, Níkolas Carneiro, Ronnie Alves
A Geometric Framework for Adversarial Vulnerability in Machine Learning
Brian Bell
The More the Merrier? Navigating Accuracy vs. Energy Efficiency Design Trade-Offs in Ensemble Learning Systems
Rafiullah Omar, Justus Bogner, Henry Muccini, Patricia Lago, Silverio Martínez-Fernández, Xavier Franch
Precise and Efficient Orbit Prediction in LEO with Machine Learning using Exogenous Variables
Francisco Caldas, Cláudia Soares
Development of Machine Learning Classifiers for Blood-based Diagnosis and Prognosis of Suspected Acute Infections and Sepsis
Ljubomir Buturovic, Michael Mayhew, Roland Luethy, Kirindi Choi, Uros Midic, Nandita Damaraju, Yehudit Hasin-Brumshtein, Amitesh Pratap, Rhys M. Adams, Joao Fonseca, Ambika Srinath, Paul Fleming, Claudia Pereira, Oliver Liesenfeld, Purvesh Khatri, Timothy Sweeney
Artificial intelligence and machine learning generated conjectures with TxGraffiti
Randy Davila
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