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
Selecting Interpretability Techniques for Healthcare Machine Learning models
Daniel Sierra-Botero, Ana Molina-Taborda, Mario S. Valdés-Tresanco, Alejandro Hernández-Arango, Leonardo Espinosa-Leal, Alexander Karpenko, Olga Lopez-Acevedo
Challenges in explaining deep learning models for data with biological variation
Lenka Tětková, Erik Schou Dreier, Robin Malm, Lars Kai Hansen
Between Randomness and Arbitrariness: Some Lessons for Reliable Machine Learning at Scale
A. Feder Cooper
Are We There Yet? A Brief Survey of Music Emotion Prediction Datasets, Models and Outstanding Challenges
Jaeyong Kang, Dorien Herremans
An AI Architecture with the Capability to Explain Recognition Results
Paul Whitten, Francis Wolff, Chris Papachristou