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
AI Guided Early Screening of Cervical Cancer
Dharanidharan S I, Suhitha Renuka S V, Ajishi Singh, Sheena Christabel Pravin
Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions
Daniel M. Jimenez G., David Solans, Mikko Heikkila, Andrea Vitaletti, Nicolas Kourtellis, Aris Anagnostopoulos, Ioannis Chatzigiannakis
A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning
Caleb J.S. Barr, Olivia Erdelyi, Paul D. Docherty, Randolph C. Grace
Fitting Multiple Machine Learning Models with Performance Based Clustering
Mehmet Efe Lorasdagi, Ahmet Berker Koc, Ali Taha Koc, Suleyman Serdar Kozat
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
Canyu Chen, Jian Yu, Shan Chen, Che Liu, Zhongwei Wan, Danielle Bitterman, Fei Wang, Kai Shu
Neuro-Symbolic Rule Lists
Sascha Xu, Nils Philipp Walter, Jilles Vreeken