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
Computing Within Limits: An Empirical Study of Energy Consumption in ML Training and Inference
Ioannis Mavromatis, Kostas Katsaros, Aftab Khan
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik, Dieter Theiler, Armin Haberl, Stefan Thalmann, Dominik Kowald
Intelligent Interface: Enhancing Lecture Engagement with Didactic Activity Summaries
Anna Wróblewska, Marcel Witas, Kinga Frańczak, Arkadiusz Kniaź, Siew Ann Cheong, Tan Seng Chee, Janusz Hołyst, Marcin Paprzycki
Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data
Sajad Amouei Sheshkal, Morten Gundersen, Michael Alexander Riegler, Øygunn Aass Utheim, Kjell Gunnar Gundersen, Hugo Lewi Hammer
Machine Learning Techniques in Automatic Music Transcription: A Systematic Survey
Fatemeh Jamshidi, Gary Pike, Amit Das, Richard Chapman
Recent Advances in Traffic Accident Analysis and Prediction: A Comprehensive Review of Machine Learning Techniques
Noushin Behboudi, Sobhan Moosavi, Rajiv Ramnath
A Systematic Literature Review on the Use of Machine Learning in Software Engineering
Nyaga Fred, I. O. Temkin
On the Consistency of Fairness Measurement Methods for Regression Tasks
Abdalwahab Almajed, Maryam Tabar, Peyman Najafirad
ModSec-Learn: Boosting ModSecurity with Machine Learning
Christian Scano, Giuseppe Floris, Biagio Montaruli, Luca Demetrio, Andrea Valenza, Luca Compagna, Davide Ariu, Luca Piras, Davide Balzarotti, Battista Biggio
Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and MOFs using Machine Learning
Subhadeep Dasgupta, Amal R S, Prabal K. Maiti
Identifiable Causal Representation Learning: Unsupervised, Multi-View, and Multi-Environment
Julius von Kügelgen
Data Collection and Labeling Techniques for Machine Learning
Qianyu Huang, Tongfang Zhao
Enhancing supply chain security with automated machine learning
Haibo Wang, Lutfu S. Sua, Bahram Alidaee
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera, Thomas E. Markland
Machine Learning and Optimization Techniques for Solving Inverse Kinematics in a 7-DOF Robotic Arm
Enoch Adediran, Salem Ameen
Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimation
Nikolas Koutsoubis, Yasin Yilmaz, Ravi P. Ramachandran, Matthew Schabath, Ghulam Rasool
Unsupervised explainable activity prediction in competitive Nordic Walking from experimental data
Silvia García-Méndez, Francisco de Arriba-Pérez, Francisco J. González-Castaño, Javier Vales-Alonso
GFM4MPM: Towards Geospatial Foundation Models for Mineral Prospectivity Mapping
Angel Daruna, Vasily Zadorozhnyy, Georgina Lukoczki, Han-Pang Chiu
Automatic generation of insights from workers' actions in industrial workflows with explainable Machine Learning
Francisco de Arriba-Pérez, Silvia García-Méndez, Javier Otero-Mosquera, Francisco J. González-Castaño, Felipe Gil-Castiñeira
TREE: Tree Regularization for Efficient Execution
Lena Schmid, Daniel Biebert, Christian Hakert, Kuan-Hsun Chen, Michel Lang, Markus Pauly, Jian-Jia Chen