BACKGROUND
Precision medicine requires delivery and tracking of genome-informed risk assessments (GIRAs), often documented in unstructured electronic health record (EHR) notes, making large-scale evaluation reliant on labor-intensive manual chart review. Large language models (LLMs) offer a promising approach to automa...
Yi Xin, K. W. Davis, Wu-Chen Su et al.· JAMIA Journal of the America...· 0 citations
Video and audio are perceived together, yet most generative models treat them in isolation. We examine methods that model the two modalities jointly, generate one from the other, or edit them in a coupled manner, organized around a single question: how is the output kept coherent across modalities in time and semantics...
Abhinav Sharma, S. Navuluru, Wang Wei et al.· 0 citations
Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incorporating relevant procedural and domain knowledge. Expert-authored skills are expensive to produce, and the resulting artifacts are not optimized for the specific model t...
Graph neural networks (GNNs) have become a prominent approach for developing fast, topology-aware surrogates in electric power systems, supporting tasks such as power flow (PF) analysis, optimal power flow (OPF) estimation, and cascading failure analysis (CFA). Despite this growing use, formally verifying GNN-based mod...
Anne M. Tumlin, Ben Wooding, Zhenxuan Shao et al.· SAIV· 1 citation
A semantic enrichment framework that extends raw graph topology with function-level attributes, combining lightweight metadata with LLM-based code embeddings is proposed, with which classification performs better under distribution shift compared to model-based approaches, and consistently further enhances robustness w...
Ngoc N. Tran, Anwar Said, Waseem Abbas et al.· 0 citations
This paper introduces AutoKD, a multi-agent framework for autonomous knowledge discovery that is both computational and cumulative, allowing validated findings to persist and inform subsequent inquiry.
RAD is proposed, a rule-augmented relational anomaly detector that combines heterogeneous graph representation learning with refined symbolic rule signals and improves anomaly ranking over flattened tabular detectors and relational baselines under natural class imbalance.
Noah Dahle, Anne M. Tumlin, N. Tran et al.· 0 citations
A common layer equation is introduced that represents covered architectures through seven components: an update domain, channel set, propagation bank, per-channel message maps, channel-fusion operator, ego/residual map, and update map, which exposes the empirical inverse problem of mapping measurable graph and task pro...
S. Navuluru, Siddhartha Shankar Das, B. Ni et al.· 0 citations
This work introduces the problem of personalized auto-research, which conditions every stage of the research process on a representation of the individual researcher, and proposes a general and flexible framework that threads a graph-grounded researcher context through retrieval, hypothesis search, experimentation, wri...
B. Ni, Franck Dernoncourt, Hong-Jie Chen et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
