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
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
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