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
Model Ratatouille: Recycling Diverse Models for Out-of-Distribution Generalization
Alexandre Ramé, Kartik Ahuja, Jianyu Zhang, Matthieu Cord, Léon Bottou, David Lopez-Paz
A Comparison Between Tsetlin Machines and Deep Neural Networks in the Context of Recommendation Systems
Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma
Provable Fairness for Neural Network Models using Formal Verification
Giorgian Borca-Tasciuc, Xingzhi Guo, Stanley Bak, Steven Skiena
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models
Jean-Thomas Baillargeon, Luc Lamontagne
Shapley variable importance cloud for machine learning models
Yilin Ning, Mingxuan Liu, Nan Liu