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
Asset Ownership Identification: Using machine learning to predict enterprise asset ownership
Craig Jacobik
Small jet engine reservoir computing digital twin
C. J. Wright, N. Biederman, B. Gyovai, D. J. Gauthier, J. P. Wilhelm
Reliable Probabilistic Classification with Neural Networks
Harris Papadopoulos
Optimization meets Machine Learning: An Exact Algorithm for Semi-Supervised Support Vector Machines
Veronica Piccialli, Jan Schwiddessen, Antonio M. Sudoso
Machine Learning for the Multi-Dimensional Bin Packing Problem: Literature Review and Empirical Evaluation
Wenjie Wu, Changjun Fan, Jincai Huang, Zhong Liu, Junchi Yan
PySCIPOpt-ML: Embedding Trained Machine Learning Models into Mixed-Integer Programs
Mark Turner, Antonia Chmiela, Thorsten Koch, Michael Winkler
A Novel Metric for Measuring Data Quality in Classification Applications (extended version)
Jouseau Roxane, Salva Sébastien, Samir Chafik
Machine Learning and Citizen Science Approaches for Monitoring the Changing Environment
Sulong Zhou
Human-computer Interaction for Brain-inspired Computing Based on Machine Learning And Deep Learning: A Review
Bihui Yu, Sibo Zhang, Lili Zhou, Jingxuan Wei, Linzhuang Sun, Liping Bu
Experimental Investigation of Machine Learning based Soft-Failure Management using the Optical Spectrum
Lars E. Kruse, Sebastian Kühl, Annika Dochhan, Stephan Pachnicke
Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving
Yinwei Dai, Rui Pan, Anand Iyer, Kai Li, Ravi Netravali
A Review of Machine Learning Methods Applied to Video Analysis Systems
Marios S. Pattichis, Venkatesh Jatla, Alvaro E. Ullao Cerna
Artificial Neural Nets and the Representation of Human Concepts
Timo Freiesleben
Disentangling CO Chemistry in a Protoplanetary Disk Using Explanatory Machine Learning Techniques
Amina Diop, Ilse Cleeves, Dana Anderson, Jamila Pegues, Adele Plunkett