H2o: Heavy-hitter oracle for efficient generative inference of large language models
Abstract Large Language Models (LLMs), despite their recent impressive accomplishments,
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
are notably cost-prohibitive to deploy, particularly for applications involving long-content …
Strategies for implementing machine learning algorithms in the clinical practice of radiology
Despite recent advancements in machine learning (ML) applications in health care, there
have been few benefits and improvements to clinical medicine in the hospital setting. To …
have been few benefits and improvements to clinical medicine in the hospital setting. To …
The emergence of essential sparsity in large pre-trained models: The weights that matter
Large pre-trained transformers are $\textit {show-stealer} $ in modern-day deep learning,
and it becomes crucial to comprehend the parsimonious patterns that exist within them as …
and it becomes crucial to comprehend the parsimonious patterns that exist within them as …
Retrieving evidence from ehrs with llms: Possibilities and challenges
H Ahsan, DJ McInerney, J Kim, C Potter… - arXiv preprint arXiv …, 2023 - arxiv.org
Unstructured Electronic Health Record (EHR) data often contains critical information
complementary to imaging data that would inform radiologists' diagnoses. However, time …
complementary to imaging data that would inform radiologists' diagnoses. However, time …
Instant soup: Cheap pruning ensembles in a single pass can draw lottery tickets from large models
Large pre-trained transformers have been receiving explosive attention in the past few
years, due to their acculturation for numerous downstream applications via fine-tuning, but …
years, due to their acculturation for numerous downstream applications via fine-tuning, but …
On the opportunities and risks of foundation models for natural language processing in radiology
WF Wiggins, AS Tejani - Radiology: Artificial Intelligence, 2022 - pubs.rsna.org
Ali S. Tejani, MD, is a radiology resident at the University of Texas Southwestern Medical
Center in Dallas, Tex, where he founded the Imaging Informatics and Business Intelligence …
Center in Dallas, Tex, where he founded the Imaging Informatics and Business Intelligence …
Sparse moe as the new dropout: Scaling dense and self-slimmable transformers
Despite their remarkable achievement, gigantic transformers encounter significant
drawbacks, including exorbitant computational and memory footprints during training, as …
drawbacks, including exorbitant computational and memory footprints during training, as …
Performance of multiple pretrained BERT models to automate and accelerate data annotation for large datasets
Purpose To develop and evaluate domain-specific and pretrained bidirectional encoder
representations from transformers (BERT) models in a transfer learning task on varying …
representations from transformers (BERT) models in a transfer learning task on varying …
[HTML][HTML] Less likely brainstorming: Using language models to generate alternative hypotheses
A human decision-maker benefits the most from an AI assistant that corrects for their biases.
For problems such as generating interpretation of a radiology report given findings, a system …
For problems such as generating interpretation of a radiology report given findings, a system …
Information extraction from weakly structured radiological reports with natural language queries
Objectives Provide physicians and researchers an efficient way to extract information from
weakly structured radiology reports with natural language processing (NLP) machine …
weakly structured radiology reports with natural language processing (NLP) machine …