ELIXR: Towards a general purpose X-ray artificial intelligence system through alignment of large language models and radiology vision encoders

no code implementations • 2 Aug 2023 • Shawn Xu, Lin Yang, Christopher Kelly, Marcin Sieniek, Timo Kohlberger, Martin Ma, Wei-Hung Weng, Atilla Kiraly, Sahar Kazemzadeh, Zakkai Melamed, Jungyeon Park, Patricia Strachan, Yun Liu, Chuck Lau, Preeti Singh, Christina Chen, Mozziyar Etemadi, Sreenivasa Raju Kalidindi, Yossi Matias, Katherine Chou, Greg S. Corrado, Shravya Shetty, Daniel Tse, Shruthi Prabhakara, Daniel Golden, Rory Pilgrim, Krish Eswaran, Andrew Sellergren

In this work, we present an approach, which we call Embeddings for Language/Image-aligned X-Rays, or ELIXR, that leverages a language-aligned image encoder combined or grafted onto a fixed LLM, PaLM 2, to perform a broad range of chest X-ray tasks.

Contrastive Learning Question Answering +2

Probabilistic Forecast-based Portfolio Optimization of Electricity Demand at Low Aggregation Levels

no code implementations • 18 Apr 2023 • Jungyeon Park, Estêvão Alvarenga, Jooyoung Jeon, Ran Li, Fotios Petropoulos, Hokyun Kim, Kwangwon Ahn

Using probabilistic load forecasts produced by either ARMA-GARCH models or kernel density estimation (KDE), we propose three approaches to creating a portfolio of residential households' demand: Forecast Validated, Seasonal Residual, and Seasonal Similarity.

Computational Efficiency Density Estimation +2