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
Traffic Cameras to detect inland waterway barge traffic: An Application of machine learning
Geoffery Agorku, Sarah Hernandez PhD, Maria Falquez, Subhadipto Poddar PhD, Kwadwo Amankwah-Nkyi
On the Stability of a non-hyperbolic nonlinear map with non-bounded set of non-isolated fixed points with applications to Machine Learning
Roberta Hansen, Matias Vera, Lautaro Estienne, Luciana Ferrer, Pablo Piantanida
Credence: Augmenting Datacenter Switch Buffer Sharing with ML Predictions
Vamsi Addanki, Maciej Pacut, Stefan Schmid
Model-Agnostic Interpretation Framework in Machine Learning: A Comparative Study in NBA Sports
Shun Liu
Novel End-to-End Production-Ready Machine Learning Flow for Nanolithography Modeling and Correction
Mohamed S. E. Habib, Hossam A. H. Fahmy, Mohamed F. Abu-ElYazeed
Machine Learning in Robotic Ultrasound Imaging: Challenges and Perspectives
Yuan Bi, Zhongliang Jiang, Felix Duelmer, Dianye Huang, Nassir Navab
Improving automatic detection of driver fatigue and distraction using machine learning
Dongjiang Wu
The Compute Divide in Machine Learning: A Threat to Academic Contribution and Scrutiny?
Tamay Besiroglu, Sage Andrus Bergerson, Amelia Michael, Lennart Heim, Xueyun Luo, Neil Thompson
Fast & Fair: Efficient Second-Order Robust Optimization for Fairness in Machine Learning
Allen Minch, Hung Anh Vu, Anne Marie Warren
Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review
Ihsane Gryech, Chaimae Assad, Mounir Ghogho, Abdellatif Kobbane
Using AI/ML to Find and Remediate Enterprise Secrets in Code & Document Sharing Platforms
Gregor Kerr, David Algorry, Senad Ibraimoski, Peter Maciver, Sean Moran
Automating Leukemia Diagnosis with Autoencoders: A Comparative Study
Minoo Sayyadpour, Nasibe Moghaddamniya, Touraj Banirostam
KAXAI: An Integrated Environment for Knowledge Analysis and Explainable AI
Saikat Barua, Dr. Sifat Momen
Machine Learning (ML)-assisted Beam Management in millimeter (mm)Wave Distributed Multiple Input Multiple Output (D-MIMO) systems
Karthik R M, Dhiraj Nagaraja Hegde, Muris Sarajlic, Abhishek Sarkar
Mapping Walnut Water Stress with High Resolution Multispectral UAV Imagery and Machine Learning
Kaitlyn Wang, Yufang Jin