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
Religious Affiliation in the Twenty-First Century: A Machine Learning Perspective on the World Value Survey
Elaheh Jafarigol, William Keely, Tess Hartog, Tom Welborn, Peyman Hekmatpour, Theodore B. Trafalis
Observational and Experimental Insights into Machine Learning-Based Defect Classification in Wafers
Kamal Taha
Machine learning in physics: a short guide
Francisco A. Rodrigues
Advantages of Machine Learning in Bus Transport Analysis
Amirsadegh Roshanzamir
A Comprehensive Study of Privacy Risks in Curriculum Learning
Joann Qiongna Chen, Xinlei He, Zheng Li, Yang Zhang, Zhou Li
Applications of Machine Learning in Biopharmaceutical Process Development and Manufacturing: Current Trends, Challenges, and Opportunities
Thanh Tung Khuat, Robert Bassett, Ellen Otte, Alistair Grevis-James, Bogdan Gabrys
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