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
Lecture notes on rough paths and applications to machine learning
Thomas Cass, Cristopher Salvi
"Hey..! This medicine made me sick": Sentiment Analysis of User-Generated Drug Reviews using Machine Learning Techniques
Abhiram B. Nair, Abhinand K., Anamika U., Denil Tom Jaison, Ajitha V., V. S. Anoop
A Cyber Manufacturing IoT System for Adaptive Machine Learning Model Deployment by Interactive Causality Enabled Self-Labeling
Yutian Ren, Yuqi He, Xuyin Zhang, Aaron Yen, G. P. Li
Explaining Indian Stock Market through Geometry of Scale free Networks
Pawanesh, Charu Sharma, Niteesh Sahni
Predictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
Taminul Islam, Md. Alif Sheakh, Mst. Sazia Tahosin, Most. Hasna Hena, Shopnil Akash, Yousef A. Bin Jardan, Gezahign Fentahun Wondmie, Hiba-Allah Nafidi, Mohammed Bourhia
An Automated Machine Learning Approach to Inkjet Printed Component Analysis: A Step Toward Smart Additive Manufacturing
Abhishek Sahu, Peter H. Aaen, Praveen Damacharla
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
Florentina Tatrin Kurniati, Daniel HF Manongga, Eko Sediyono, Sri Yulianto Joko Prasetyo, Roy Rudolf Huizen
A Systems Theoretic Approach to Online Machine Learning
Anli du Preez, Peter A. Beling, Tyler Cody
On Extending the Automatic Test Markup Language (ATML) for Machine Learning
Tyler Cody, Bingtong Li, Peter A. Beling
Integrating Hyperparameter Search into Model-Free AutoML with Context-Free Grammars
Hernán Ceferino Vázquez, Jorge Sanchez, Rafael Carrascosa
Using Large Language Models to Enrich the Documentation of Datasets for Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Site-specific Deterministic Temperature and Humidity Forecasts with Explainable and Reliable Machine Learning
MengMeng Han, Tennessee Leeuwenburg, Brad Murphy
Machine Learning and Data Analysis Using Posets: A Survey
Arnauld Mesinga Mwafise
Assessing ML Classification Algorithms and NLP Techniques for Depression Detection: An Experimental Case Study
Giuliano Lorenzoni, Cristina Tavares, Nathalia Nascimento, Paulo Alencar, Donald Cowan
Identifying Climate Targets in National Laws and Policies using Machine Learning
Matyas Juhasz, Tina Marchand, Roshan Melwani, Kalyan Dutia, Sarah Goodenough, Harrison Pim, Henry Franks
Stochastic Constrained Decentralized Optimization for Machine Learning with Fewer Data Oracles: a Gradient Sliding Approach
Hoang Huy Nguyen, Yan Li, Tuo Zhao