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
Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies
Theophilus G. Baidoo, Ashley Obeng
Accelerating the discovery of low-energy structure configurations: a computational approach that integrates first-principles calculations, Monte Carlo sampling, and Machine Learning
Md Rajib Khan Musa, Yichen Qian, Jie Peng, David Cereceda
Military Applications of Machine Learning: A Bibliometric Perspective
José Javier Galán, Ramón Alberto Carrasco, Antonio LaTorre
Towards a Categorical Foundation of Deep Learning: A Survey
Francesco Riccardo Crescenzi
GRU-D Characterizes Age-Specific Temporal Missingness in MIMIC-IV
Niklas Giesa, Mert Akgül, Sebastian Daniel Boie, Felix Balzer
A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research
Teresa Salazar, Helder Araújo, Alberto Cano, Pedro Henriques Abreu
Tadashi: Enabling AI-Based Automated Code Generation With Guaranteed Correctness
Emil Vatai, Aleksandr Drozd, Ivan R. Ivanov, Yinghao Ren, Mohamed Wahib
Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging
Ethan Kane Waters, Carla Chia-ming Chen, Mostafa Rahimi Azghadi
Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns
Keyu Chen, Ziqian Bi, Tianyang Wang, Yizhu Wen, Pohsun Feng, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Ming Liu
Review Non-convex Optimization Method for Machine Learning
Greg B Fotopoulos, Paul Popovich, Nicholas Hall Papadopoulos
shapiq: Shapley Interactions for Machine Learning
Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier
Causal Inference Tools for a Better Evaluation of Machine Learning
Michaël Soumm
A Federated Learning Platform as a Service for Advancing Stroke Management in European Clinical Centers
Diogo Reis Santos, Albert Sund Aillet, Antonio Boiano, Usevalad Milasheuski, Lorenzo Giusti, Marco Di Gennaro, Sanaz Kianoush, Luca Barbieri, Monica Nicoli, Michele Carminati, Alessandro E. C. Redondi, Stefano Savazzi, Luigi Serio
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications
Pohsun Feng, Ziqian Bi, Yizhu Wen, Xuanhe Pan, Benji Peng, Ming Liu, Jiawei Xu, Keyu Chen, Junyu Liu, Caitlyn Heqi Yin, Sen Zhang, Jinlang Wang, Qian Niu, Ming Li, Tianyang Wang
Enhancing End Stage Renal Disease Outcome Prediction: A Multi-Sourced Data-Driven Approach
Yubo Li, Rema Padman