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
Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology
Qingsong Xu, Yilei Shi, Jonathan Bamber, Ye Tuo, Ralf Ludwig, Xiao Xiang Zhu
Human-in-the-loop: The future of Machine Learning in Automated Electron Microscopy
Sergei V. Kalinin, Yongtao Liu, Arpan Biswas, Gerd Duscher, Utkarsh Pratiush, Kevin Roccapriore, Maxim Ziatdinov, Rama Vasudevan
Interventions Against Machine-Assisted Statistical Discrimination
John Y. Zhu
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Dominique Beaini, Shenyang Huang, Joao Alex Cunha, Zhiyi Li, Gabriela Moisescu-Pareja, Oleksandr Dymov, Samuel Maddrell-Mander, Callum McLean, Frederik Wenkel, Luis Müller, Jama Hussein Mohamud, Ali Parviz, Michael Craig, Michał Koziarski, Jiarui Lu, Zhaocheng Zhu, Cristian Gabellini, Kerstin Klaser, Josef Dean, Cas Wognum, Maciej Sypetkowski, Guillaume Rabusseau, Reihaneh Rabbany, Jian Tang, Christopher Morris, Ioannis Koutis, Mirco Ravanelli, Guy Wolf, Prudencio Tossou, Hadrien Mary, Therence Bois, Andrew Fitzgibbon, Błażej Banaszewski, Chad Martin, Dominic Masters
Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms
Dennis Klau, Marc Zöller, Christian Tutschku
Cost-Effective Retraining of Machine Learning Models
Ananth Mahadevan, Michael Mathioudakis
Genetic prediction of quantitative traits: a machine learner's guide focused on height
Lucie Bourguignon, Caroline Weis, Catherine R. Jutzeler, Michael Adamer
CLASSify: A Web-Based Tool for Machine Learning
Aaron D. Mullen, Samuel E. Armstrong, Jeff Talbert, V. K. Cody Bumgardner
MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation
Qian Huang, Jian Vora, Percy Liang, Jure Leskovec
Formal and Practical Elements for the Certification of Machine Learning Systems
Jean-Guillaume Durand, Arthur Dubois, Robert J. Moss
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland
Credit card score prediction using machine learning models: A new dataset
Anas Arram, Masri Ayob, Musatafa Abbas Abbood Albadr, Alaa Sulaiman, Dheeb Albashish
Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
Mojtaba A. Farahani, M. R. McCormick, Ramy Harik, Thorsten Wuest
Kernel-based function learning in dynamic and non stationary environments
Alberto Giaretta, Mauro Bisiacco, Gianluigi Pillonetto
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning
Jayasudha M, Ayesha Shaik, Gaurav Pendharkar, Soham Kumar, Muhesh Kumar B, Sudharshanan Balaji
Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
Hassan Jameel Asghar, Zhigang Lu, Zhongrui Zhao, Dali Kaafar
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches
Srilekha Mamidala