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
Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China
Joseph Cameron, Jiaee Cheong, Micol Spitale, Hatice Gunes
Autonomous, Self-driving Multi-Step Growth of Semiconductor Heterostructures Guided by Machine Learning
Chao Shen, Wenkang Zhan, Hongyu Sun, Kaiyao Xin, Bo Xu, Zhanguo Wang, Chao Zhao
Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services
Shaopeng Fu, Xuexue Sun, Ke Qing, Tianhang Zheng, Di Wang
On Using Quasirandom Sequences in Machine Learning for Model Weight Initialization
Andriy Miranskyy, Adam Sorrenti, Viral Thakar
Machine Learning Applications in Medical Prognostics: A Comprehensive Review
Michael Fascia
Methods to improve run time of hydrologic models: opportunities and challenges in the machine learning era
Supath Dhital
Trustworthy Machine Learning under Social and Adversarial Data Sources
Han Shao
Accelerating Domain-Aware Electron Microscopy Analysis Using Deep Learning Models with Synthetic Data and Image-Wide Confidence Scoring
Matthew J. Lynch, Ryan Jacobs, Gabriella Bruno, Priyam Patki, Dane Morgan, Kevin G. Field
A Structured Framework for Predicting Sustainable Aviation Fuel Properties using Liquid-Phase FTIR and Machine Learning
Ana E. Comesana, Sharon S. Chen, Kyle E. Niemeyer, Vi H. Rapp
TASI Lectures on Physics for Machine Learning
Jim Halverson
Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI
Lingxi Cui, Huan Li, Ke Chen, Lidan Shou, Gang Chen
TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors
Zhaolan Huang, Adrien Tousnakhoff, Polina Kozyr, Roman Rehausen, Felix Bießmann, Robert Lachlan, Cedric Adjih, Emmanuel Baccelli