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
Understanding Addition in Transformers
Philip Quirke, Fazl Barez
SecurityNet: Assessing Machine Learning Vulnerabilities on Public Models
Boyang Zhang, Zheng Li, Ziqing Yang, Xinlei He, Michael Backes, Mario Fritz, Yang Zhang
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time series
Ioannis Nasios, Konstantinos Vogklis
Julearn: an easy-to-use library for leakage-free evaluation and inspection of ML models
Sami Hamdan, Shammi More, Leonard Sasse, Vera Komeyer, Kaustubh R. Patil, Federico Raimondo
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho, M. Fouesneau, B. Nord, J. E. G. Peek, M. Walmsley, J. F. Wu, C. Avestruz, T. Buck, M. Brescia, D. P. Finkbeiner, A. D. Goulding, T. Kacprzak, P. Melchior, M. Pasquato, N. Ramachandra, Y. -S. Ting, G. van de Ven, S. Villar, V. A. Villar, E. Zinger
When Machine Learning Models Leak: An Exploration of Synthetic Training Data
Manel Slokom, Peter-Paul de Wolf, Martha Larson
Strategies and impact of learning curve estimation for CNN-based image classification
Laura Didyk, Brayden Yarish, Michael A. Beck, Christopher P. Bidinosti, Christopher J. Henry
Dealing with zero-inflated data: achieving SOTA with a two-fold machine learning approach
Jože M. Rožanec, Gašper Petelin, João Costa, Blaž Bertalanič, Gregor Cerar, Marko Guček, Gregor Papa, Dunja Mladenić
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus, Laura Freeman
Disk failure prediction based on multi-layer domain adaptive learning
Guangfu Gao, Peng Wu, Hussain Dawood
Detecting and Learning Out-of-Distribution Data in the Open world: Algorithm and Theory
Yiyou Sun