Rajesh Ranganath | Developing the mathematical foundations of explainability ... | CGSI 2025

Rajesh Ranganath | Developing the mathematical foundations of explainability and using them to catch an LLM in a lie | CGSI 2025 Related Papers: Tozzo, V., Zhang, L. H., Ranganath, R., & Higgins, J. M. (2025). Transformer-based artificial intelligence on single-cell clinical data for homeostatic mechanism inference and rational biomarker discovery. medRxiv, 2025-03. Jethani, N., Puli, A., Zhang, H., Garber, L., Jankelson, L., Aphinyanaphongs, Y., & Ranganath, R. (2022). New-onset diabetes assessment using artificial intelligence-enhanced electrocardiography. arXiv preprint arXiv:2205.02900. Zhang, Lily H., Veronica Tozzo, John M. Higgins, and Rajesh Ranganath. "Set Norm and Equivariant Skip Connections: Putting the Deep in Deep Sets." arXiv preprint arXiv:2206.11925 (2022). Singhal, R., Sudarshan, M., Mahishi, A., Kaushik, S., Ginocchio, L., Tong, A., ... & Chopra, S. (2023). On the feasibility of machine learning augmented magnetic resonance for point-of-care identification of disease. arXiv preprint arXiv:2301.11962.