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
Cross-Prediction-Powered Inference
Tijana Zrnic, Emmanuel J. Candès
Collaborative Distributed Machine Learning
David Jin, Niclas Kannengießer, Sascha Rank, Ali Sunyaev
Review of Machine Learning Methods for Additive Manufacturing of Functionally Graded Materials
Mohammad Karimzadeh, Aleksandar Vakanski, Fei Xu, Xinchang Zhang
Unmasking the Chameleons: A Benchmark for Out-of-Distribution Detection in Medical Tabular Data
Mohammad Azizmalayeri, Ameen Abu-Hanna, Giovanni Ciná
Systematic Sampling and Validation of Machine Learning-Parameterizations in Climate Models
Jerry Lin, Sungduk Yu, Tom Beucler, Pierre Gentine, David Walling, Mike Pritchard
Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective
Maitham G. Yousif, Fadhil G. Al-Amran, Hector J. Castro
Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysis
Hector J. Castro, Maitham G. Yousif
Resilience of Deep Learning applications: a systematic literature review of analysis and hardening techniques
Cristiana Bolchini, Luca Cassano, Antonio Miele
Model Share AI: An Integrated Toolkit for Collaborative Machine Learning Model Development, Provenance Tracking, and Deployment in Python
Heinrich Peters, Michael Parrott
MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Angelo Yamachui Sitcheu, Nils Friederich, Simon Baeuerle, Oliver Neumann, Markus Reischl, Ralf Mikut
Method and Validation for Optimal Lineup Creation for Daily Fantasy Football Using Machine Learning and Linear Programming
Joseph M. Mahoney, Tomasz B. Paniak
Monitoring Machine Learning Models: Online Detection of Relevant Deviations
Florian Heinrichs
Automated Detection of Persistent Inflammatory Biomarkers in Post-COVID-19 Patients Using Machine Learning Techniques
Ghizal Fatima, Fadhil G. Al-Amran, Maitham G. Yousif
Can-SAVE: Mass Cancer Risk Prediction via Survival Analysis Variables and EHR
Petr Philonenko, Vladimir Kokh, Pavel Blinov
Predicting environment effects on breast cancer by implementing machine learning
Muhammad Shoaib Farooq, Mehreen Ilyas
Driving behavior-guided battery health monitoring for electric vehicles using machine learning
Nanhua Jiang, Jiawei Zhang, Weiran Jiang, Yao Ren, Jing Lin, Edwin Khoo, Ziyou Song
Can neural networks count digit frequency?
Padmaksh Khandelwal