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
Comparative Analysis on Snowmelt-Driven Streamflow Forecasting Using Machine Learning Techniques
Ukesh Thapa, Bipun Man Pati, Samit Thapa, Dhiraj Pyakurel, Anup Shrestha
Capturing Momentum: Tennis Match Analysis Using Machine Learning and Time Series Theory
Jingdi Lei, Tianqi Kang, Yuluan Cao, Shiwei Ren
From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility Gap
Tianqi Kou
A Guide to Feature Importance Methods for Scientific Inference
Fiona Katharina Ewald, Ludwig Bothmann, Marvin N. Wright, Bernd Bischl, Giuseppe Casalicchio, Gunnar König
Beyond development: Challenges in deploying machine learning models for structural engineering applications
Mohsen Zaker Esteghamati, Brennan Bean, Henry V. Burton, M. Z. Naser
Generalizing Machine Learning Evaluation through the Integration of Shannon Entropy and Rough Set Theory
Olga Cherednichenko, Dmytro Chernyshov, Dmytro Sytnikov, Polina Sytnikova
Enhancing Financial Inclusion and Regulatory Challenges: A Critical Analysis of Digital Banks and Alternative Lenders Through Digital Platforms, Machine Learning, and Large Language Models Integration
Luke Lee
Designing an Intelligent Parcel Management System using IoT & Machine Learning
Mohit Gupta, Nitesh Garg, Jai Garg, Vansh Gupta, Devraj Gautam
Using Game Engines and Machine Learning to Create Synthetic Satellite Imagery for a Tabletop Verification Exercise
Johannes Hoster, Sara Al-Sayed, Felix Biessmann, Alexander Glaser, Kristian Hildebrand, Igor Moric, Tuong Vy Nguyen
OmniLytics+: A Secure, Efficient, and Affordable Blockchain Data Market for Machine Learning through Off-Chain Processing
Songze Li, Mingzhe Liu, Mengqi Chen
Analytical results for uncertainty propagation through trained machine learning regression models
Andrew Thompson
Explainable Machine Learning System for Predicting Chronic Kidney Disease in High-Risk Cardiovascular Patients
Nantika Nguycharoen
LMEraser: Large Model Unlearning through Adaptive Prompt Tuning
Jie Xu, Zihan Wu, Cong Wang, Xiaohua Jia
Beam Training in mmWave Vehicular Systems: Machine Learning for Decoupling Beam Selection
Ibrahim Kilinc, Ryan M. Dreifuerst, Junghoon Kim, Robert W. Heath
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition
Jiawen Xu
The Impact of Machine Learning on Society: An Analysis of Current Trends and Future Implications
Md Kamrul Hossain Siam, Manidipa Bhattacharjee, Shakik Mahmud, Md. Saem Sarkar, Md. Masud Rana
AI Competitions and Benchmarks: Dataset Development
Romain Egele, Julio C. S. Jacques Junior, Jan N. van Rijn, Isabelle Guyon, Xavier Baró, Albert Clapés, Prasanna Balaprakash, Sergio Escalera, Thomas Moeslund, Jun Wan
Machine Learning Techniques for Python Source Code Vulnerability Detection
Talaya Farasat, Joachim Posegga
High Significant Fault Detection in Azure Core Workload Insights
Pranay Lohia, Laurent Boue, Sharath Rangappa, Vijay Agneeswaran
Emerging Platforms Meet Emerging LLMs: A Year-Long Journey of Top-Down Development
Siyuan Feng, Jiawei Liu, Ruihang Lai, Charlie F. Ruan, Yong Yu, Lingming Zhang, Tianqi Chen