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
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
Introducing CausalBench: A Flexible Benchmark Framework for Causal Analysis and Machine Learning
Ahmet Kapkiç, Pratanu Mandal, Shu Wan, Paras Sheth, Abhinav Gorantla, Yoonhyuk Choi, Huan Liu, K. Selçuk Candan
Machine Learning and Econometric Approaches to Fiscal Policies: Understanding Industrial Investment Dynamics in Uruguay (1974-2010)
Diego Vallarino
Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
Petr Philonenko, Sergey Postovalov
DEMAU: Decompose, Explore, Model and Analyse Uncertainties
Arthur Hoarau, Vincent Lemaire
Rapid Parameter Estimation for Extreme Mass Ratio Inspirals Using Machine Learning
Bo Liang, Hong Guo, Tianyu Zhao, He wang, Herik Evangelinelis, Yuxiang Xu, Chang liu, Manjia Liang, Xiaotong Wei, Yong Yuan, Peng Xu, Minghui Du, Wei-Liang Qian, Ziren Luo
Multi-scale decomposition of sea surface height snapshots using machine learning
Jingwen Lyu, Yue Wang, Christian Pedersen, Spencer Jones, Dhruv Balwada
Machine Learning and Constraint Programming for Efficient Healthcare Scheduling
Aymen Ben Said, Malek Mouhoub
Revisiting Static Feature-Based Android Malware Detection
Md Tanvirul Alam, Dipkamal Bhusal, Nidhi Rastogi
Applied Federated Model Personalisation in the Industrial Domain: A Comparative Study
Ilias Siniosoglou, Vasileios Argyriou, George Fragulis, Panagiotis Fouliras, Georgios Th. Papadopoulos, Anastasios Lytos, Panagiotis Sarigiannidis
A study on deep feature extraction to detect and classify Acute Lymphoblastic Leukemia (ALL)
Sabit Ahamed Preanto (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md. Taimur Ahad (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Yousuf Rayhan Emon (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Sumaya Mustofa (4IR Research Cell Daffodil International University, Dhaka, Bangladesh), Md Alamin (4IR Research Cell Daffodil International University, Dhaka, Bangladesh)
Advancements in Gesture Recognition Techniques and Machine Learning for Enhanced Human-Robot Interaction: A Comprehensive Review
Sajjad Hussain, Khizer Saeed, Almas Baimagambetov, Shanay Rab, Md Saad
Ransomware Detection Using Machine Learning in the Linux Kernel
Adrian Brodzik, Tomasz Malec-Kruszyński, Wojciech Niewolski, Mikołaj Tkaczyk, Krzysztof Bocianiak, Sok-Yen Loui
A Latent Implicit 3D Shape Model for Multiple Levels of Detail
Benoit Guillard, Marc Habermann, Christian Theobalt, Pascal Fua
Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learning
Peyman Milanfar, Mauricio Delbracio