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
A Flexible Cell Classification for ML Projects in Jupyter Notebooks
Miguel Perez, Selin Aydin, Horst Lichter
DiabetesNet: A Deep Learning Approach to Diabetes Diagnosis
Zeyu Zhang, Khandaker Asif Ahmed, Md Rakibul Hasan, Tom Gedeon, Md Zakir Hossain
On the nonconvexity of some push-forward constraints and its consequences in machine learning
Lucas de Lara, Mathis Deronzier, Alberto González-Sanz, Virgile Foy
Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprints
Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris
UCINet0: A Machine Learning based Receiver for 5G NR PUCCH Format 0
Anil Kumar Yerrapragada, Jeeva Keshav Sattianarayanin, Radha Krishna Ganti
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Apolline Mellot, Antoine Collas, Sylvain Chevallier, Denis Engemann, Alexandre Gramfort
Machine learning and information theory concepts towards an AI Mathematician
Yoshua Bengio, Nikolay Malkin
Architectural Blueprint For Heterogeneity-Resilient Federated Learning
Satwat Bashir, Tasos Dagiuklas, Kasra Kassai, Muddesar Iqbal
Storm Surge Modeling in the AI ERA: Using LSTM-based Machine Learning for Enhancing Forecasting Accuracy
Stefanos Giaremis, Noujoud Nader, Clint Dawson, Hartmut Kaiser, Carola Kaiser, Efstratios Nikidis
Your device may know you better than you know yourself -- continuous authentication on novel dataset using machine learning
Pedro Gomes do Nascimento, Pidge Witiak, Tucker MacCallum, Zachary Winterfeldt, Rushit Dave
Eternal Sunshine of the Mechanical Mind: The Irreconcilability of Machine Learning and the Right to be Forgotten
Meem Arafat Manab
Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training
Tanveer Khan, Mindaugas Budzys, Khoa Nguyen, Antonis Michalas
Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing
Sergio Rubio-Martín, María Teresa García-Ordás, Martín Bayón-Gutiérrez, Natalia Prieto-Fernández, José Alberto Benítez-Andrades
Single Transit Detection In Kepler With Machine Learning And Onboard Spacecraft Diagnostics
Matthew T. Hansen, Jason A. Dittmann
Citizen Science and Machine Learning for Research and Nature Conservation: The Case of Eurasian Lynx, Free-ranging Rodents and Insects
Kinga Skorupska, Rafał Stryjek, Izabela Wierzbowska, Piotr Bebas, Maciej Grzeszczuk, Piotr Gago, Jarosław Kowalski, Maciej Krzywicki, Jagoda Lazarek, Wiesław Kopeć
Data Collaboration Analysis Over Matrix Manifolds
Keiyu Nosaka, Akiko Yoshise
Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
Xiaonan Xu, Yichao Wu, Penghao Liang, Yuhang He, Han Wang