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Found 6 papers, 4 papers with code
ViLMedic: a framework for research at the intersection of vision and language in medical AI
There is a growing need to model interactions between data modalities (e. g., vision, language) — both to improve AI predictions on existing tasks and to enable new applications.
Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes
Code synthesis is cheap, but far less accurate than directly processing each document with the LLM.
HAPI: A Large-scale Longitudinal Dataset of Commercial ML API Predictions
HAPI is the first large-scale dataset of ML API usages and is a unique resource for studying ML-as-a-service (MLaaS).
DataPerf: Benchmarks for Data-Centric AI Development
Machine learning (ML) research has generally focused on models, while the most prominent datasets have been employed for everyday ML tasks without regard for the breadth, difficulty, and faithfulness of these datasets to the underlying problem.
Domino: Discovering Systematic Errors with Cross-Modal Embeddings
In this work, we address these challenges by first designing a principled evaluation framework that enables a quantitative comparison of SDMs across 1, 235 slice discovery settings in three input domains (natural images, medical images, and time-series data).
OncoNet: Weakly Supervised Siamese Network to automate cancer treatment response assessment between longitudinal FDG PET/CT examinations
FDG PET/CT imaging is a resource intensive examination critical for managing malignant disease and is particularly important for longitudinal assessment during therapy.
