9 citations · 18 across the 4 of their papers we have counts for
4 papers
Validating Large Language Models with ReLM
Michael Kuchnik, Virginia Smith, George Amvrosiadis
Although large language models (LLMs) have been touted for their ability to generate natural-sounding text, there are growing concerns around possible negative effects of LLMs such…
Plumber: Diagnosing and Removing Performance Bottlenecks in Machine Learning Data Pipelines
Michael Kuchnik, Ana Klimovic, Jiri Simsa +2
Input pipelines, which ingest and transform input data, are an essential part of training Machine Learning (ML) models. However, it is challenging to implement efficient input pipe…
Unleashing In-network Computing on Scientific Workloads
Daehyeok Kim, Ankush Jain, Zaoxing Liu +4
Many recent efforts have shown that in-network computing can benefit various datacenter applications. In this paper, we explore a relatively less-explored domain which we argue can…
Progressive Compressed Records: Taking a Byte out of Deep Learning Data
Michael Kuchnik, George Amvrosiadis, Virginia Smith
Deep learning accelerators efficiently train over vast and growing amounts of data, placing a newfound burden on commodity networks and storage devices. A common approach to conser…