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
Sparks of Quantum Advantage and Rapid Retraining in Machine Learning
William Troy
GraphScale: A Framework to Enable Machine Learning over Billion-node Graphs
Vipul Gupta, Xin Chen, Ruoyun Huang, Fanlong Meng, Jianjun Chen, Yujun Yan
Fever Detection with Infrared Thermography: Enhancing Accuracy through Machine Learning Techniques
Parsa Razmara, Tina Khezresmaeilzadeh, B. Keith Jenkins
A Comprehensive Guide to Combining R and Python code for Data Science, Machine Learning and Reinforcement Learning
Alejandro L. García Navarro, Nataliia Koneva, Alfonso Sánchez-Macián, José Alberto Hernández
Two new feature selection methods based on learn-heuristic techniques for breast cancer prediction: A comprehensive analysis
Kamyab Karimi, Ali Ghodratnama, Reza Tavakkoli-Moghaddam
Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reduction
José Daniel Pascual-Triana, Alberto Fernández, Paulo Novais, Francisco Herrera
Enhancing Variable Importance in Random Forests: A Novel Application of Global Sensitivity Analysis
Giulia Vannucci, Roberta Siciliano, Andrea Saltelli
Machine Learning for Dynamic Management Zone in Smart Farming
Chamil Kulatunga, Sahraoui Dhelim, Tahar Kechadi
On Diversity in Discriminative Neural Networks
Brahim Oubaha, Claude Berrou, Xueyao Ji, Yehya Nasser, Raphaël Le Bidan
A Comprehensive Sustainable Framework for Machine Learning and Artificial Intelligence
Roberto Pagliari, Peter Hill, Po-Yu Chen, Maciej Dabrowny, Tingsheng Tan, Francois Buet-Golfouse
LLM Inference Serving: Survey of Recent Advances and Opportunities
Baolin Li, Yankai Jiang, Vijay Gadepally, Devesh Tiwari
Information-Theoretic Foundations for Machine Learning
Hong Jun Jeon, Benjamin Van Roy
Questionable practices in machine learning
Gavin Leech, Juan J. Vazquez, Niclas Kupper, Misha Yagudin, Laurence Aitchison
MetaLLM: A High-performant and Cost-efficient Dynamic Framework for Wrapping LLMs
Quang H. Nguyen, Duy C. Hoang, Juliette Decugis, Saurav Manchanda, Nitesh V. Chawla, Khoa D. Doan
Physics-Informed Machine Learning for Smart Additive Manufacturing
Rahul Sharma, Maziar Raissi, Y. B. Guo
Impacts of Data Preprocessing and Hyperparameter Optimization on the Performance of Machine Learning Models Applied to Intrusion Detection Systems
Mateus Guimarães Lima, Antony Carvalho, João Gabriel Álvares, Clayton Escouper das Chagas, Ronaldo Ribeiro Goldschmidt
Brain Tumor Classification From MRI Images Using Machine Learning
Vidhyapriya Ranganathan, Celshiya Udaiyar, Jaisree Jayanth, Meghaa P, Srija B, Uthra S
Leveraging Hybrid Intelligence Towards Sustainable and Energy-Efficient Machine Learning
Daniel Geissler, Paul Lukowicz