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
Inferring Stellar Parameters from Iodine-Imprinted Keck/HIRES Spectra with Machine Learning
Jude Gussman, Malena Rice
ML-On-Rails: Safeguarding Machine Learning Models in Software Systems A Case Study
Hala Abdelkader, Mohamed Abdelrazek, Scott Barnett, Jean-Guy Schneider, Priya Rani, Rajesh Vasa
Automated Machine Learning for Positive-Unlabelled Learning
Jack D. Saunders, Alex A. Freitas
Knowledge-Informed Machine Learning for Cancer Diagnosis and Prognosis: A review
Lingchao Mao, Hairong Wang, Leland S. Hu, Nhan L Tran, Peter D Canoll, Kristin R Swanson, Jing Li
A Closer Look at AUROC and AUPRC under Class Imbalance
Matthew B. A. McDermott, Lasse Hyldig Hansen, Haoran Zhang, Giovanni Angelotti, Jack Gallifant
Cheetah: Bridging the Gap Between Machine Learning and Particle Accelerator Physics with High-Speed, Differentiable Simulations
Jan Kaiser, Chenran Xu, Annika Eichler, Andrea Santamaria Garcia
When eBPF Meets Machine Learning: On-the-fly OS Kernel Compartmentalization
Zicheng Wang, Tiejin Chen, Qinrun Dai, Yueqi Chen, Hua Wei, Qingkai Zeng
A Deep Learning Approach Towards Student Performance Prediction in Online Courses: Challenges Based on a Global Perspective
Abdallah Moubayed, MohammadNoor Injadat, Nouh Alhindawi, Ghassan Samara, Sara Abuasal, Raed Alazaidah
Decoupling Decision-Making in Fraud Prevention through Classifier Calibration for Business Logic Action
Emanuele Luzio, Moacir Antonelli Ponti, Christian Ramirez Arevalo, Luis Argerich
Machine Learning to Promote Translational Research: Predicting Patent and Clinical Trial Inclusion in Dementia Research
Matilda Beinat, Julian Beinat, Mohammed Shoaib, Jorge Gomez Magenti
Feature Network Methods in Machine Learning and Applications
Xinying Mu, Mark Kon
T-PRIME: Transformer-based Protocol Identification for Machine-learning at the Edge
Mauro Belgiovine, Joshua Groen, Miquel Sirera, Chinenye Tassie, Ayberk Yarkın Yıldız, Sage Trudeau, Stratis Ioannidis, Kaushik Chowdhury
Sea wave data reconstruction using micro-seismic measurements and machine learning methods
Lorenzo Iafolla, Emiliano Fiorenza, Massimo Chiappini, Cosmo Carmisciano, Valerio Antonio Iafolla
Air Quality Forecasting Using Machine Learning: A Global perspective with Relevance to Low-Resource Settings
Mulomba Mukendi Christian, Hyebong Choi
Entangling Machine Learning with Quantum Tensor Networks
Constantijn van der Poel, Dan Zhao
FlopPITy: Enabling self-consistent exoplanet atmospheric retrievals with machine learning
Francisco Ardévol Martínez, Michiel Min, Daniela Huppenkothen, Inga Kamp, Paul I. Palmer
Differential Equations for Continuous-Time Deep Learning
Lars Ruthotto
Machine Learning Applications in Traumatic Brain Injury: A Spotlight on Mild TBI
Hanem Ellethy, Shekhar S. Chandra, Viktor Vegh