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
Asymptotically Fair Participation in Machine Learning Models: an Optimal Control Perspective
Zhuotong Chen, Qianxiao Li, Zheng Zhang
GSAP-NER: A Novel Task, Corpus, and Baseline for Scholarly Entity Extraction Focused on Machine Learning Models and Datasets
Wolfgang Otto, Matthäus Zloch, Lu Gan, Saurav Karmakar, Stefan Dietze
Comparing Differentiable Logics for Learning Systems: A Research Preview
Thomas Flinkow, Barak A. Pearlmutter, Rosemary Monahan
A Comparative Analysis of Machine Learning Models for Early Detection of Hospital-Acquired Infections
Ethan Harvey, Junzi Dong, Erina Ghosh, Ali Samadani
Human-in-the-loop: Towards Label Embeddings for Measuring Classification Difficulty
Katharina Hechinger, Christoph Koller, Xiao Xiang Zhu, Göran Kauermann
Explainable History Distillation by Marked Temporal Point Process
Sishun Liu, Ke Deng, Yan Wang, Xiuzhen Zhang
Optimising Human-AI Collaboration by Learning Convincing Explanations
Alex J. Chan, Alihan Huyuk, Mihaela van der Schaar
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local Explanations
Koorosh Aslansefat, Mojgan Hashemian, Martin Walker, Mohammed Naveed Akram, Ioannis Sorokos, Yiannis Papadopoulos
Novel models for fatigue life prediction under wideband random loads based on machine learning
Hong Sun, Yuanying Qiu, Jing Li, Jin Bai, Ming Peng
Improving Cardiovascular Disease Prediction Through Comparative Analysis of Machine Learning Models: A Case Study on Myocardial Infarction
Jonayet Miah, Duc M Ca, Md Abu Sayed, Ehsanur Rashid Lipu, Fuad Mahmud, S M Yasir Arafat
Machine learning for accuracy in density functional approximations
Johannes Voss