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
Machine-learning based high-bandwidth magnetic sensing
Galya Haim, Stefano Martina, John Howell, Nir Bar-Gill, Filippo Caruso
How to predict on-road air pollution based on street view images and machine learning: a quantitative analysis of the optimal strategy
Hui Zhong, Di Chen, Pengqin Wang, Wenrui Wang, Shaojie Shen, Yonghong Liu, Meixin Zhu
Stronger Baseline Models -- A Key Requirement for Aligning Machine Learning Research with Clinical Utility
Nathan Wolfrath, Joel Wolfrath, Hengrui Hu, Anjishnu Banerjee, Anai N. Kothari
An Explainable Machine Learning Approach to Traffic Accident Fatality Prediction
Md. Asif Khan Rifat, Ahmedul Kabir, Armana Sabiha Huq
Bushfire Severity Modelling and Future Trend Prediction Across Australia: Integrating Remote Sensing and Machine Learning
Shouthiri Partheepan, Farzad Sanati, Jahan Hassan
Training Datasets Generation for Machine Learning: Application to Vision Based Navigation
Jérémy Lebreton, Ingo Ahrns, Roland Brochard, Christoph Haskamp, Hans Krüger, Matthieu Le Goff, Nicolas Menga, Nicolas Ollagnier, Ralf Regele, Francesco Capolupo, Massimo Casasco
Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations
Jiaqi Ding, Tingting Dan, Ziquan Wei, Hyuna Cho, Paul J. Laurienti, Won Hwa Kim, Guorong Wu
Machine Learning and Theory Ladenness -- A Phenomenological Account
Alberto Termine, Emanuele Ratti, Alessandro Facchini
Machine Learning for Public Good: Predicting Urban Crime Patterns to Enhance Community Safety
Sia Gupta, Simeon Sayer
TPFL: Tsetlin-Personalized Federated Learning with Confidence-Based Clustering
Rasoul Jafari Gohari, Laya Aliahmadipour, Ezat Valipour
Machine Learning to Detect Anxiety Disorders from Error-Related Negativity and EEG Signals
Ramya Chandrasekar, Md Rakibul Hasan, Shreya Ghosh, Tom Gedeon, Md Zakir Hossain
Spiers Memorial Lecture: How to do impactful research in artificial intelligence for chemistry and materials science
Austin Cheng, Cher Tian Ser, Marta Skreta, Andrés Guzmán-Cordero, Luca Thiede, Andreas Burger, Abdulrahman Aldossary, Shi Xuan Leong, Sergio Pablo-García, Felix Strieth-Kalthoff, Alán Aspuru-Guzik
From Bytes to Bites: Using Country Specific Machine Learning Models to Predict Famine
Salloni Kapoor, Simeon Sayer
Fault Analysis And Predictive Maintenance Of Induction Motor Using Machine Learning
Kavana Venkatesh, Neethi M
Deep Fast Machine Learning Utils: A Python Library for Streamlined Machine Learning Prototyping
Fabi Prezja
Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
Siddhant Dutta, Pavana P Karanth, Pedro Maciel Xavier, Iago Leal de Freitas, Nouhaila Innan, Sadok Ben Yahia, Muhammad Shafique, David E. Bernal Neira
Predicting Trust In Autonomous Vehicles: Modeling Young Adult Psychosocial Traits, Risk-Benefit Attitudes, And Driving Factors With Machine Learning
Robert Kaufman, Emi Lee, Manas Satish Bedmutha, David Kirsh, Nadir Weibel
AutoIRT: Calibrating Item Response Theory Models with Automated Machine Learning
James Sharpnack, Phoebe Mulcaire, Klinton Bicknell, Geoff LaFlair, Kevin Yancey