Machine Learning Model
Machine learning models aim to create systems that can learn from data and make predictions or decisions without explicit programming. Current research emphasizes improving model accuracy, interpretability, and robustness, focusing on architectures like deep neural networks, decision tree ensembles, and transformer models, as well as exploring decentralized learning and techniques for mitigating biases and vulnerabilities. These advancements are crucial for diverse applications, ranging from optimizing resource management (e.g., smart irrigation) to improving healthcare diagnostics and enhancing the security and trustworthiness of AI systems.
Papers
Advancing oncology with federated learning: transcending boundaries in breast, lung, and prostate cancer. A systematic review
Anshu Ankolekar, Sebastian Boie, Maryam Abdollahyan, Emanuela Gadaleta, Seyed Alireza Hasheminasab, Guang Yang, Charles Beauville, Nikolaos Dikaios, George Anthony Kastis, Michael Bussmann, Sara Khalid, Hagen Kruger, Philippe Lambin, Giorgos Papanastasiou
Robustness investigation of quality measures for the assessment of machine learning models
Thomas Most, Lars Gräning, Sebastian Wolff
4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models
Kylen Solvik, Stephen G. Penny, Stephan Hoyer
Operational range bounding of spectroscopy models with anomaly detection
Luís F. Simões, Pierluigi Casale, Marília Felismino, Kai Hou Yip, Ingo P. Waldmann, Giovanna Tinetti, Theresa Lueftinger
SLO-aware GPU Frequency Scaling for Energy Efficient LLM Inference Serving
Andreas Kosmas Kakolyris, Dimosthenis Masouros, Petros Vavaroutsos, Sotirios Xydis, Dimitrios Soudris
Spurious Correlations in Concept Drift: Can Explanatory Interaction Help?
Cristiana Lalletti, Stefano Teso
Machine Learning Models for the Identification of Cardiovascular Diseases Using UK Biobank Data
Sheikh Mohammed Shariful Islam, Moloud Abrar, Teketo Tegegne, Liliana Loranjo, Chandan Karmakar, Md Abdul Awal, Md. Shahadat Hossain, Muhammad Ashad Kabir, Mufti Mahmud, Abbas Khosravi, George Siopis, Jeban C Moses, Ralph Maddison
Representation Magnitude has a Liability to Privacy Vulnerability
Xingli Fang, Jung-Eun Kim
Error Detection and Constraint Recovery in Hierarchical Multi-Label Classification without Prior Knowledge
Joshua Shay Kricheli, Khoa Vo, Aniruddha Datta, Spencer Ozgur, Paulo Shakarian
${\it Asparagus}$: A Toolkit for Autonomous, User-Guided Construction of Machine-Learned Potential Energy Surfaces
Kai Töpfer, Luis Itza Vazquez-Salazar, Markus Meuwly
A General Framework for Data-Use Auditing of ML Models
Zonghao Huang, Neil Zhenqiang Gong, Michael K. Reiter