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
Precision Machine Learning
Eric J. Michaud, Ziming Liu, Max Tegmark
Logic-Based Explainability in Machine Learning
Joao Marques-Silva
Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Data
Bhushan Chaudhari, Akash Agarwal, Tanmoy Bhowmik
Federated Learning and Meta Learning: Approaches, Applications, and Directions
Xiaonan Liu, Yansha Deng, Arumugam Nallanathan, Mehdi Bennis
Explaining automated gender classification of human gait
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Raberger, Sebastian Lapuschkin, Wojciech Samek, Wolfgang I. Schöllhorn, Christian Breiteneder, Brian Horsak
Explaining machine learning models for age classification in human gait analysis
Djordje Slijepcevic, Fabian Horst, Marvin Simak, Sebastian Lapuschkin, Anna-Maria Raberger, Wojciech Samek, Christian Breiteneder, Wolfgang I. Schöllhorn, Matthias Zeppelzauer, Brian Horsak
ELIAS: End-to-End Learning to Index and Search in Large Output Spaces
Nilesh Gupta, Patrick H. Chen, Hsiang-Fu Yu, Cho-Jui Hsieh, Inderjit S Dhillon