2 citations · 2 across the 3 of their papers we have counts for
6 papers
M3: Conversational LLMs Simplify Secure Clinical Data Access, Understanding, and Analysis
Rafi Al Attrach, Pedro Moreira, Rajna Fani +3
Large-scale clinical databases offer opportunities for medical research, but their complexity creates barriers to effective use. The Medical Information Mart for Intensive Care (MI…
Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets
Rafi Al Attrach, Rajna Fani, Sebastian Lobentanzer +17
Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON-LD-based format that makes dataset discovery, automated ingestion, and re…
Quantum Kernel Advantage over Classical Collapse in Medical Foundation Model Embeddings
Sebastian Cajas Ordóñez, Felipe Ocampo Osorio, Dax Enshan Koh +10
We provide evidence of quantum kernel advantage under noiseless simulation in binary insurance classification on MIMIC-CXR chest radiographs using quantum support vector machines (…
Rethinking Tokenization for Clinical Time Series: When Less is More
Rafi Al Attrach, Rajna Fani, David Restrepo +2
Tokenization strategies shape how models process electronic health records, yet fair comparisons of their effectiveness remain limited. We present a systematic evaluation of tokeni…
Coefficient of Variation Masking: A Volatility-Aware Strategy for EHR Foundation Models
Rajna Fani, Rafi Al Attrach, David Restrepo +3
Masked autoencoders (MAEs) are increasingly applied to electronic health records (EHR) for learning general-purpose representations that support diverse clinical tasks. However, ex…
The Amazon Nova Family of Models: Technical Report and Model Card
Amazon AGI, Aaron Langford, Aayush Shah +783
We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…