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
PPI++: Efficient Prediction-Powered Inference
Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic
Identifying Alzheimer Disease Dementia Levels Using Machine Learning Methods
Md Gulzar Hussain, Ye Shiren
An energy-based comparative analysis of common approaches to text classification in the Legal domain
Sinan Gultekin, Achille Globo, Andrea Zugarini, Marco Ernandes, Leonardo Rigutini
An Innovative Tool for Uploading/Scraping Large Image Datasets on Social Networks
Nicolò Fabio Arceri, Oliver Giudice, Sebastiano Battiato
Accelerating Electronic Stopping Power Predictions by 10 Million Times with a Combination of Time-Dependent Density Functional Theory and Machine Learning
Logan Ward, Ben Blaiszik, Cheng-Wei Lee, Troy Martin, Ian Foster, André Schleife
Software Repositories and Machine Learning Research in Cyber Security
Mounika Vanamala, Keith Bryant, Alex Caravella
Machine Learning Without a Processor: Emergent Learning in a Nonlinear Electronic Metamaterial
Sam Dillavou, Benjamin D Beyer, Menachem Stern, Andrea J Liu, Marc Z Miskin, Douglas J Durian
Real-Time Magnetic Tracking and Diagnosis of COVID-19 via Machine Learning
Dang Nguyen, Phat K. Huynh, Vinh Duc An Bui, Kee Young Hwang, Nityanand Jain, Chau Nguyen, Le Huu Nhat Minh, Le Van Truong, Xuan Thanh Nguyen, Dinh Hoang Nguyen, Le Tien Dung, Trung Q. Le, Manh-Huong Phan
Model-driven Engineering for Machine Learning Components: A Systematic Literature Review
Hira Naveed, Chetan Arora, Hourieh Khalajzadeh, John Grundy, Omar Haggag
OpenForest: A data catalogue for machine learning in forest monitoring
Arthur Ouaknine, Teja Kattenborn, Etienne Laliberté, David Rolnick
A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning
Samuel E. Otto, Nicholas Zolman, J. Nathan Kutz, Steven L. Brunton
Machine learning for accuracy in density functional approximations
Johannes Voss
Seeking Truth and Beauty in Flavor Physics with Machine Learning
Konstantin T. Matchev, Katia Matcheva, Pierre Ramond, Sarunas Verner
Linked Papers With Code: The Latest in Machine Learning as an RDF Knowledge Graph
Michael Färber, David Lamprecht
Investigating Relative Performance of Transfer and Meta Learning
Benji Alwis
Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications
Marcus Haywood-Alexander, Wei Liu, Kiran Bacsa, Zhilu Lai, Eleni Chatzi
Machine learning refinement of in situ images acquired by low electron dose LC-TEM
Hiroyasu Katsuno, Yuki Kimura, Tomoya Yamazaki, Ichigaku Takigawa
Fraud Analytics Using Machine-learning & Engineering on Big Data (FAME) for Telecom
Sudarson Roy Pratihar, Subhadip Paul, Pranab Kumar Dash, Amartya Kumar Das
MLatom 3: Platform for machine learning-enhanced computational chemistry simulations and workflows
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou