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
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
LoRaWAN Based Dynamic Noise Mapping with Machine Learning for Urban Noise Enforcement
H. Emre Erdem, Henry Leung
A Scalable Tool For Analyzing Genomic Variants Of Humans Using Knowledge Graphs and Machine Learning
Shivika Prasanna, Ajay Kumar, Deepthi Rao, Eduardo Simoes, Praveen Rao
Be aware of overfitting by hyperparameter optimization!
Igor V. Tetko, Ruud van Deursen, Guillaume Godin
Two-Phase Segmentation Approach for Accurate Left Ventricle Segmentation in Cardiac MRI using Machine Learning
Maria Tamoor, Abbas Raza Ali, Philemon Philip, Ruqqayia Adil, Rabia Shahid, Asma Naseer
Machine Learning for Predicting Chaotic Systems
Christof Schötz, Alistair White, Maximilian Gelbrecht, Niklas Boers
Reconstructing Global Daily CO2 Emissions via Machine Learning
Tao Li, Lixing Wang, Zihan Qiu, Philippe Ciais, Taochun Sun, Matthew W. Jones, Robbie M. Andrew, Glen P. Peters, Piyu ke, Xiaoting Huang, Robert B. Jackson, Zhu Liu
Denoising ESG: quantifying data uncertainty from missing data with Machine Learning and prediction intervals
Sergio Caprioli, Jacopo Foschi, Riccardo Crupi, Alessandro Sabatino