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
Visualization for Trust in Machine Learning Revisited: The State of the Field in 2023
Angelos Chatzimparmpas, Kostiantyn Kucher, Andreas Kerren
CICLe: Conformal In-Context Learning for Largescale Multi-Class Food Risk Classification
Korbinian Randl, John Pavlopoulos, Aron Henriksson, Tony Lindgren
Automated data processing and feature engineering for deep learning and big data applications: a survey
Alhassan Mumuni, Fuseini Mumuni
Potential of Domain Adaptation in Machine Learning in Ecology and Hydrology to Improve Model Extrapolability
Haiyang Shi
Machine Learning and Vision Transformers for Thyroid Carcinoma Diagnosis: A review
Yassine Habchi, Hamza Kheddar, Yassine Himeur, Abdelkrim Boukabou, Ammar Chouchane, Abdelmalik Ouamane, Shadi Atalla, Wathiq Mansoor
How do Machine Learning Projects use Continuous Integration Practices? An Empirical Study on GitHub Actions
João Helis Bernardo, Daniel Alencar da Costa, Sérgio Queiroz de Medeiros, Uirá Kulesza
depyf: Open the Opaque Box of PyTorch Compiler for Machine Learning Researchers
Kaichao You, Runsheng Bai, Meng Cao, Jianmin Wang, Ion Stoica, Mingsheng Long
Moments of Clarity: Streamlining Latent Spaces in Machine Learning using Moment Pooling
Rikab Gambhir, Athis Osathapan, Jesse Thaler
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Tobias Hille, Maximilian Stubbemann, Tom Hanika
Data augmentation with automated machine learning: approaches and performance comparison with classical data augmentation methods
Alhassan Mumuni, Fuseini Mumuni
Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine Learning: A Karnataka TB Data Study at a Scale
SeshaSai Nath Chinagudaba, Darshan Gera, Krishna Kiran Vamsi Dasu, Uma Shankar S, Kiran K, Anil Singarajpure, Shivayogappa. U, Somashekar N, Vineet Kumar Chadda, Sharath B N
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Na Li, Chunyi Zhou, Yansong Gao, Hui Chen, Anmin Fu, Zhi Zhang, Yu Shui
Machine Learning Techniques for Sensor-based Human Activity Recognition with Data Heterogeneity -- A Review
Xiaozhou Ye, Kouichi Sakurai, Nirmal Nair, Kevin I-Kai Wang
A Machine learning and Empirical Bayesian Approach for Predictive Buying in B2B E-commerce
Tuhin Subhra De, Pranjal Singh, Alok Patel
When Eye-Tracking Meets Machine Learning: A Systematic Review on Applications in Medical Image Analysis
Sahar Moradizeyveh, Mehnaz Tabassum, Sidong Liu, Robert Ahadizad Newport, Amin Beheshti, Antonio Di Ieva
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou, Ivan Brugere, Dana Dachman-Soled, Min Wu
Machine Learning for Soccer Match Result Prediction
Rory Bunker, Calvin Yeung, Keisuke Fujii