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
Predictive Maintenance Model Based on Anomaly Detection in Induction Motors: A Machine Learning Approach Using Real-Time IoT Data
Sergio F. Chevtchenko, Monalisa C. M. dos Santos, Diego M. Vieira, Ricardo L. Mota, Elisson Rocha, Bruna V. Cruz, Danilo Araújo, Ermeson Andrade
Statistical inference using machine learning and classical techniques based on accumulated local effects (ALE)
Chitu Okoli
Enhancing ML model accuracy for Digital VLSI circuits using diffusion models: A study on synthetic data generation
Prasha Srivastava, Pawan Kumar, Zia Abbas
Evolutionary Dynamic Optimization and Machine Learning
Abdennour Boulesnane
Real-Time Event Detection with Random Forests and Temporal Convolutional Networks for More Sustainable Petroleum Industry
Yuanwei Qu, Baifan Zhou, Arild Waaler, David Cameron
Eliciting Model Steering Interactions from Users via Data and Visual Design Probes
Anamaria Crisan, Maddie Shang, Eric Brochu
Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal
Susan Athey, Niall Keleher, Jann Spiess
Counting and Algorithmic Generalization with Transformers
Simon Ouellette, Rolf Pfister, Hansueli Jud
Defect Analysis of 3D Printed Cylinder Object Using Transfer Learning Approaches
Md Manjurul Ahsan, Shivakumar Raman, Zahed Siddique
Divorce Prediction with Machine Learning: Insights and LIME Interpretability
Md Manjurul Ahsan
GRASP: Accelerating Shortest Path Attacks via Graph Attention
Zohair Shafi, Benjamin A. Miller, Ayan Chatterjee, Tina Eliassi-Rad, Rajmonda S. Caceres
A Review of Machine Learning Techniques in Imbalanced Data and Future Trends
Elaheh Jafarigol, Theodore Trafalis
The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
Thomas Decker, Ralf Gross, Alexander Koebler, Michael Lebacher, Ronald Schnitzer, Stefan H. Weber
Energy Estimates Across Layers of Computing: From Devices to Large-Scale Applications in Machine Learning for Natural Language Processing, Scientific Computing, and Cryptocurrency Mining
Sadasivan Shankar
Histopathological Image Classification and Vulnerability Analysis using Federated Learning
Sankalp Vyas, Amar Nath Patra, Raj Mani Shukla
Machine Learning Methods for Background Potential Estimation in 2DEGs
Carlo da Cunha, Nobuyuki Aoki, David Ferry, Kevin Vora, Yu Zhang