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Vy A. Vo

Intel Labs

17 papers hereh-index 17913 citations38 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author14
  • last author2

Across the 16 of 17 papers where every author was matched, so the position is known.

fields
  • cs.CL8
  • cs.LG4
  • cs.AI2
  • cs.DC1
  • cs.PL1
  • cs.SE1
affiliations
  • Intel Labs
same name
  • Vy A. Vo — 1 paper

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202026
most citedOMPGPT: A Generative Pre-trained Transformer Model for OpenMP

24 citations · 94 across the 16 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2025

Large Language Models as Model Organisms for Human Associative Learning

Camila Kolling, Vy Ai Vo, Mariya Toneva

Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Testing hypotheses on how rep…

cs.LG2024★ 6 cited

The Landscape and Challenges of HPC Research and LLMs

Le Chen, Nesreen K. Ahmed, Akash Dutta +14

Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and prompt-based techniques ha…

cs.LG2022★ 2 cited

Memory-Augmented Graph Neural Networks: A Brain-Inspired Review

Guixiang Ma, Vy A. Vo, Theodore Willke +1

We provide a comprehensive review of the existing literature on memory-augmented GNNs. We review these works through the lens of psychology and neuroscience, which has several esta…

cs.LG2021★ 11 cited

Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay

Hsiang-Yun Sherry Chien, Javier S. Turek, Nicole Beckage +3

Sequential information contains short- to long-range dependencies; however, learning long-timescale information has been a challenge for recurrent neural networks. Despite improvem…

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