5 papers
Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture
Sourish Dey, Aditya Kumar
Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impractical as hot-path decision…
Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
Aditya Kumar, Simon Rauch, Mario Cypko +10
We introduce Temporal Fusion Nexus (TFN), a multi-modal and task-agnostic embedding model to integrate irregular time series and unstructured clinical narratives. We analysed TFN i…
Learning temporal embeddings from electronic health records of chronic kidney disease patients
Aditya Kumar, Mario A. Cypko, Oliver Amft
We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations without compromising predictive p…
Enhancing Productivity in Database Management Through AI: A Three-Phase Approach for Database
Kushagra Parashar, Ajay Dev, Aditya Kumar +1
This paper presents a novel AI-powered framework designed to streamline database management and query optimization for PostgreSQL systems. Structured in three phases: Natural Langu…
Med-gte-hybrid: A contextual embedding transformer model for extracting actionable information from clinical texts
Aditya Kumar, Simon Rauch, Mario Cypko +1
We introduce a novel contextual embedding model med-gte-hybrid that was derived from the gte-large sentence transformer to extract information from unstructured clinical narratives…