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
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
What is the relationship between Slow Feature Analysis and the Successor Representation?
Eddie Seabrook, Laurenz Wiskott
Generalization vs. Specialization under Concept Shift
Alex Nguyen, David J. Schwab, Vudtiwat Ngampruetikorn
Stalactite: Toolbox for Fast Prototyping of Vertical Federated Learning Systems
Anastasiia Zakharova, Dmitriy Alexandrov, Maria Khodorchenko, Nikolay Butakov, Alexey Vasilev, Maxim Savchenko, Alexander Grigorievskiy
Identification and Localization of Cometary Activity in Solar System Objects with Machine Learning
Bryce T. Bolin, Michael W. Coughlin
Rethinking Conventional Wisdom in Machine Learning: From Generalization to Scaling
Lechao Xiao
AdapFair: Ensuring Continuous Fairness for Machine Learning Operations
Yinghui Huang, Zihao Tang, Xiangyu Chang
Efficient Tabular Data Preprocessing of ML Pipelines
Yu Zhu, Wenqi Jiang, Gustavo Alonso
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
Liyang Wang, Yu Cheng, Hao Gong, Jiacheng Hu, Xirui Tang, Iris Li
Explainable AI needs formal notions of explanation correctness
Stefan Haufe, Rick Wilming, Benedict Clark, Rustam Zhumagambetov, Danny Panknin, Ahcène Boubekki
Prediction and Detection of Terminal Diseases Using Internet of Medical Things: A Review
Akeem Temitope Otapo, Alice Othmani, Ghazaleh Khodabandelou, Zuheng Ming
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants
Gang Xu, Xin Zhou, Molin Wang, Boya Zhang, Wenhao Jiang, Francine Laden, Helen H. Suh, Adam A. Szpiro, Donna Spiegelman, Zuoheng Wang