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
GalaxiesML: a dataset of galaxy images, photometry, redshifts, and structural parameters for machine learning
Tuan Do (1), Bernie Boscoe (2), Evan Jones (1), Yun Qi Li (1, 3), Kevin Alfaro (1) ((1) UCLA, (2) Southern Oregon University, (3) University of Washington)
CliMB: An AI-enabled Partner for Clinical Predictive Modeling
Evgeny Saveliev, Tim Schubert, Thomas Pouplin, Vasilis Kosmoliaptsis, Mihaela van der Schaar
A Knowledge-Informed Large Language Model Framework for U.S. Nuclear Power Plant Shutdown Initiating Event Classification for Probabilistic Risk Assessment
Min Xian, Tao Wang, Sai Zhang, Fei Xu, Zhegang Ma
Using fractal dimension to predict the risk of intra cranial aneurysm rupture with machine learning
Pradyumna Elavarthi, Anca Ralescu, Mark D. Johnson, Charles J. Prestigiacomo
SMLE: Safe Machine Learning via Embedded Overapproximation
Matteo Francobaldi, Michele Lombardi
Novel machine learning applications at the LHC
Javier M. Duarte
Machine Learning in Industrial Quality Control of Glass Bottle Prints
Maximilian Bundscherer, Thomas H. Schmitt, Tobias Bocklet
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming
Tianyang Wang, Ziqian Bi, Keyu Chen, Jiawei Xu, Qian Niu, Junyu Liu, Benji Peng, Ming Li, Sen Zhang, Xuanhe Pan, Jinlang Wang, Pohsun Feng, Yizhu Wen, Ming Liu
ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, Philip E. Tetlock
Development of an Edge Resilient ML Ensemble to Tolerate ICS Adversarial Attacks
Likai Yao, Qinxuan Shi, Zhanglong Yang, Sicong Shao, Salim Hariri
Intelligent Energy Management: Remaining Useful Life Prediction and Charging Automation System Comprised of Deep Learning and the Internet of Things
Biplov Paneru, Bishwash Paneru, DP Sharma Mainali
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors
R. Gallon, F. Schiemenz, A. Krstova, A. Menicucci, E. Gill
Website visits can predict angler presence using machine learning
Julia S. Schmid (1), Sean Simmons (2), Mark A. Lewis (1 and 3 and 4 and 5), Mark S. Poesch (5), Pouria Ramazi (6) ((1) Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, Canada, (2) Anglers Atlas, Goldstream Publishing, Prince George, British Columbia, Canada, (3) Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia, Canada, (4) Department of Biology, University of Victoria, Victoria, British Columbia, Canada, (5) Department of Biological Sciences, University of Alberta, Edmonton, Alberta, Canada, (6) Department of Mathematics and Statistics, Brock University, St. Catharines, Ontario, Canada)
Democratizing Signal Processing and Machine Learning: Math Learning Equity for Elementary and Middle School Students
Namrata Vaswani, Mohamed Y. Selim, Renee Serrell Gibert
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer
Benji Peng, Xuanhe Pan, Yizhu Wen, Ziqian Bi, Keyu Chen, Ming Li, Ming Liu, Qian Niu, Junyu Liu, Jinlang Wang, Sen Zhang, Jiawei Xu, Pohsun Feng