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
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
Congyu Fang, Adam Dziedzic, Lin Zhang, Laura Oliva, Amol Verma, Fahad Razak, Nicolas Papernot, Bo Wang
Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods
Akash K Rao, Shashank Uttrani, Vishnu K Menon, Darshil Shah, Arnav Bhavsar, Shubhajit Roy Chowdhury, Varun Dutt
Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods
Akash K Rao, Vishnu K Menon, Shashank Uttrani, Ayushman Dixit, Dipanshu Verma, Varun Dutt
Dynamic Model Switching for Improved Accuracy in Machine Learning
Syed Tahir Abbas Hasani
Exploring Prime Number Classification: Achieving High Recall Rate and Rapid Convergence with Sparse Encoding
Serin Lee, S. Kim
Explainable data-driven modeling via mixture of experts: towards effective blending of grey and black-box models
Jessica Leoni, Valentina Breschi, Simone Formentin, Mara Tanelli
Reproducibility, energy efficiency and performance of pseudorandom number generators in machine learning: a comparative study of python, numpy, tensorflow, and pytorch implementations
Benjamin Antunes, David R. C Hill
Accelerated Cloud for Artificial Intelligence (ACAI)
Dachi Chen, Weitian Ding, Chen Liang, Chang Xu, Junwei Zhang, Majd Sakr
Is K-fold cross validation the best model selection method for Machine Learning?
Juan M Gorriz, F Segovia, J Ramirez, A Ortiz, J. Suckling
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
Michal Tkáč, Jakub Sieber, Lara Kuhlmann, Matthias Brueggenolte, Alexandru Rinciog, Michael Henke, Artur M. Schweidtmann, Qinghe Gao, Maximilian F. Theisen, Radwa El Shawi
MosquIoT: A System Based on IoT and Machine Learning for the Monitoring of Aedes aegypti (Diptera: Culicidae)
Javier Aira, Teresa Olivares Montes, Francisco M. Delicado, Darìo Vezzani
Green Runner: A tool for efficient deep learning component selection
Jai Kannan
ProtAgents: Protein discovery via large language model multi-agent collaborations combining physics and machine learning
A. Ghafarollahi, M. J. Buehler
A Systematic Review of Available Datasets in Additive Manufacturing
Xiao Liu, Alessandra Mileo, Alan F. Smeaton
Fault Diagnosis on Induction Motor using Machine Learning and Signal Processing
Muhammad Samiullah, Hasan Ali, Shehryar Zahoor, Anas Ali
Machine Learning for Shipwreck Segmentation from Side Scan Sonar Imagery: Dataset and Benchmark
Advaith V. Sethuraman, Anja Sheppard, Onur Bagoren, Christopher Pinnow, Jamey Anderson, Timothy C. Havens, Katherine A. Skinner
Four Facets of Forecast Felicity: Calibration, Predictiveness, Randomness and Regret
Rabanus Derr, Robert C. Williamson
Interpretable Solutions for Breast Cancer Diagnosis with Grammatical Evolution and Data Augmentation
Yumnah Hasan, Allan de Lima, Fatemeh Amerehi, Darian Reyes Fernandez de Bulnes, Patrick Healy, Conor Ryan