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
Inferring Pluggable Types with Machine Learning
Kazi Amanul Islam Siddiqui, Martin Kellogg
Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems
Lokman Saleh, Hafedh Mili, Mounir Boukadoum, Abderrahmane Leshob
Optimization of Trajectories for Machine Learning Training in Robot Accuracy Modeling
Blake Hannaford
Physics Informed Machine Learning (PIML) methods for estimating the remaining useful lifetime (RUL) of aircraft engines
Sriram Nagaraj, Truman Hickok
MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanations
Parikshit Solunke, Vitoria Guardieiro, Joao Rulff, Peter Xenopoulos, Gromit Yeuk-Yin Chan, Brian Barr, Luis Gustavo Nonato, Claudio Silva
Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection
Lynn Vonderhaar, Timothy Elvira, Tyler Procko, Omar Ochoa
TabularMark: Watermarking Tabular Datasets for Machine Learning
Yihao Zheng, Haocheng Xia, Junyuan Pang, Jinfei Liu, Kui Ren, Lingyang Chu, Yang Cao, Li Xiong
Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints
Yunjin Tong
Machine Learning Models for Accurately Predicting Properties of CsPbCl3 Perovskite Quantum Dots
Mehmet Sıddık Çadırcı, Musa Çadırcı
A Benchmarking Study of Kolmogorov-Arnold Networks on Tabular Data
Eleonora Poeta, Flavio Giobergia, Eliana Pastor, Tania Cerquitelli, Elena Baralis
Toward data-driven research: preliminary study to predict surface roughness in material extrusion using previously published data with Machine Learning
Fátima García-Martínez, Diego Carou, Francisco de Arriba-Pérez, Silvia García-Méndez
Centimeter Positioning Accuracy using AI/ML for 6G Applications
Sai Prasanth Kotturi, Radha Krishna Ganti
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