Li Shen

Explicitly linking multimodal patient data to biomedical knowledge graphs unlocks significant gains for clinical LLMs, with performance plummeting when these crucial connections are removed.

This framework surveys manipulation, navigation, locomotion, autonomous driving, and general embodied learning, tracing technical progressions, clarifying capability requirements, and examining datasets, benchmarks, and evaluation protocols.

Auxiliary views can enhance LLM learning efficiency, revealing that reallocation of training tokens can lead to better factual recall even when using weaker teacher models.

ACT uncovers that only 97 observations drive the predictions for 221 clinical phenotypes, revealing potential shortcuts in medical imaging interpretations.

A modular deep learning framework that enhances neuroimaging task performance by leveraging optimal task sequences from over 49,000 MRIs.

InvLT achieves superior calibration performance without compromising classification accuracy or increasing parameter complexity, setting a new standard for post-hoc uncertainty calibration.