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
CloudSense: A Model for Cloud Type Identification using Machine Learning from Radar data
Mehzooz Nizar, Jha K. Ambuj, Manmeet Singh, Vaisakh S. B, G. Pandithurai
Hybrid Quantum Graph Neural Network for Molecular Property Prediction
Michael Vitz, Hamed Mohammadbagherpoor, Samarth Sandeep, Andrew Vlasic, Richard Padbury, Anh Pham
Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules
Vanni Doffini, O. Anatole von Lilienfeld, Michael A. Nash
Physics-Enhanced Machine Learning: a position paper for dynamical systems investigations
Alice Cicirello
Utilizing Large Language Models to Generate Synthetic Data to Increase the Performance of BERT-Based Neural Networks
Chancellor R. Woolsey, Prakash Bisht, Joshua Rothman, Gondy Leroy
Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs
Antonio Bikić, Sayan Mukherjee
Explainable machine learning for predicting shellfish toxicity in the Adriatic Sea using long-term monitoring data of HABs
Martin Marzidovšek, Janja Francé, Vid Podpečan, Stanka Vadnjal, Jožica Dolenc, Patricija Mozetič
Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines
Matteo Varotto, Florian Heinrichs, Timo Schuerg, Stefano Tomasin, Stefan Valentin
The Detection of KIC 1718360, A Rotating Variable with a Possible Companion, Using Machine Learning
Jakob Roche
Opportunities for machine learning in scientific discovery
Ricardo Vinuesa, Jean Rabault, Hossein Azizpour, Stefan Bauer, Bingni W. Brunton, Arne Elofsson, Elias Jarlebring, Hedvig Kjellstrom, Stefano Markidis, David Marlevi, Paola Cinnella, Steven L. Brunton
Assemblage: Automatic Binary Dataset Construction for Machine Learning
Chang Liu, Rebecca Saul, Yihao Sun, Edward Raff, Maya Fuchs, Townsend Southard Pantano, James Holt, Kristopher Micinski
IPFed: Identity protected federated learning for user authentication
Yosuke Kaga, Yusei Suzuki, Kenta Takahashi
PhilHumans: Benchmarking Machine Learning for Personal Health
Vadim Liventsev, Vivek Kumar, Allmin Pradhap Singh Susaiyah, Zixiu Wu, Ivan Rodin, Asfand Yaar, Simone Balloccu, Marharyta Beraziuk, Sebastiano Battiato, Giovanni Maria Farinella, Aki Härmä, Rim Helaoui, Milan Petkovic, Diego Reforgiato Recupero, Ehud Reiter, Daniele Riboni, Raymond Sterling
Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiation
Kevin Lange, Federico Fontana, Francesco Rossi, Mattia Varile, Giovanni Apruzzese
Position: Why We Must Rethink Empirical Research in Machine Learning
Moritz Herrmann, F. Julian D. Lange, Katharina Eggensperger, Giuseppe Casalicchio, Marcel Wever, Matthias Feurer, David Rügamer, Eyke Hüllermeier, Anne-Laure Boulesteix, Bernd Bischl
Non-Destructive Peat Analysis using Hyperspectral Imaging and Machine Learning
Yijun Yan, Jinchang Ren, Barry Harrison, Oliver Lewis, Yinhe Li, Ping Ma
A comparative study of conformal prediction methods for valid uncertainty quantification in machine learning
Nicolas Dewolf
Architecture of a Cortex Inspired Hierarchical Event Recaller
Valentin Puente Varona
Exploring Speech Pattern Disorders in Autism using Machine Learning
Chuanbo Hu, Jacob Thrasher, Wenqi Li, Mindi Ruan, Xiangxu Yu, Lynn K Paul, Shuo Wang, Xin Li