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
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez, Charlles R. A. Abreu, Stephen E. Farr, Emilio Gallicchio, Anton Gorenko, Michael M. Henry, Frank Hu, Jing Huang, Andreas Krämer, Julien Michel, Joshua A. Mitchell, Vijay S. Pande, João PGLM Rodrigues, Jaime Rodriguez-Guerra, Andrew C. Simmonett, Sukrit Singh, Jason Swails, Philip Turner, Yuanqing Wang, Ivy Zhang, John D. Chodera, Gianni De Fabritiis, Thomas E. Markland
Credit card score prediction using machine learning models: A new dataset
Anas Arram, Masri Ayob, Musatafa Abbas Abbood Albadr, Alaa Sulaiman, Dheeb Albashish
Time-Series Classification in Smart Manufacturing Systems: An Experimental Evaluation of State-of-the-Art Machine Learning Algorithms
Mojtaba A. Farahani, M. R. McCormick, Ramy Harik, Thorsten Wuest
Kernel-based function learning in dynamic and non stationary environments
Alberto Giaretta, Mauro Bisiacco, Gianluigi Pillonetto
Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning
Jayasudha M, Ayesha Shaik, Gaurav Pendharkar, Soham Kumar, Muhesh Kumar B, Sudharshanan Balaji
Practical, Private Assurance of the Value of Collaboration via Fully Homomorphic Encryption
Hassan Jameel Asghar, Zhigang Lu, Zhongrui Zhao, Dali Kaafar
Developing a Novel Holistic, Personalized Dementia Risk Prediction Model via Integration of Machine Learning and Network Systems Biology Approaches
Srilekha Mamidala
Short text classification with machine learning in the social sciences: The case of climate change on Twitter
Karina Shyrokykh, Maksym Girnyk, Lisa Dellmuth
Machine learning assist nyc subway navigation safer and faster
Wencheng Bao, Shi Feng
Secure and Effective Data Appraisal for Machine Learning
Xu Ouyang, Changhong Yang, Felix Xiaozhu Lin, Yangfeng Ji
Automatic Quality Assessment of Wikipedia Articles -- A Systematic Literature Review
Pedro Miguel Moás, Carla Teixeira Lopes
Generalized Convergence Analysis of Tsetlin Machines: A Probabilistic Approach to Concept Learning
Mohamed-Bachir Belaid, Jivitesh Sharma, Lei Jiao, Ole-Christoffer Granmo, Per-Arne Andersen, Anis Yazidi
Data Cleaning and Machine Learning: A Systematic Literature Review
Pierre-Olivier Côté, Amin Nikanjam, Nafisa Ahmed, Dmytro Humeniuk, Foutse Khomh
A Framework for Interpretability in Machine Learning for Medical Imaging
Alan Q. Wang, Batuhan K. Karaman, Heejong Kim, Jacob Rosenthal, Rachit Saluja, Sean I. Young, Mert R. Sabuncu
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning
Yeonsoo Jeon, Mattan Erez, Michael Orshansky
A Robust Machine Learning Approach for Path Loss Prediction in 5G Networks with Nested Cross Validation
Ibrahim Yazıcı, Emre Gures
Can Pre-trained Networks Detect Familiar Out-of-Distribution Data?
Atsuyuki Miyai, Qing Yu, Go Irie, Kiyoharu Aizawa