Search Results for author: Vy A. Vo

Found 10 papers, 3 papers with code

Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks

no code implementations10 Oct 2024 Mathis Pink, Vy A. Vo, Qinyuan Wu, Jianing Mu, Javier S. Turek, Uri Hasson, Kenneth A. Norman, Sebastian Michelmann, Alexander Huth, Mariya Toneva

To address the gap in evaluating memory in LLMs, we introduce Sequence Order Recall Tasks (SORT), which we adapt from tasks used to study episodic memory in cognitive psychology.

OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation

no code implementations23 Sep 2024 Tal Kadosh, Niranjan Hasabnis, Prema Soundararajan, Vy A. Vo, Mihai Capota, Nesreen Ahmed, Yuval Pinter, Gal Oren

Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core architectures.

C++ code

MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks

3 code implementations20 Dec 2023 Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Mihai Capota, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

Specifically, we start with HPC as a domain and build an HPC-specific LM, named MonoCoder, which is orders of magnitude smaller than existing LMs but delivers better performance on non-HPC and HPC codes.

Code Generation Language Modeling +1

Scope is all you need: Transforming LLMs for HPC Code

2 code implementations18 Aug 2023 Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

With easier access to powerful compute resources, there is a growing trend in the field of AI for software development to develop larger and larger language models (LLMs) to address a variety of programming tasks.

All Code Completion

Memory in humans and deep language models: Linking hypotheses for model augmentation

no code implementations4 Oct 2022 Omri Raccah, Phoebe Chen, Ted L. Willke, David Poeppel, Vy A. Vo

The computational complexity of the self-attention mechanism in Transformer models significantly limits their ability to generalize over long temporal durations.

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

no code implementations12 May 2021 Hsiang-Yun Sherry Chien, Javier S. Turek, Nicole Beckage, Vy A. Vo, Christopher J. Honey, Ted L. Willke

Altogether, we found that LSTM with the proposed forget gate can learn long-term dependencies, outperforming other recurrent networks in multiple domains; such gating mechanism can be integrated into other architectures for improving the learning of long timescale information in recurrent neural networks.

Image Classification Language Modeling +1

Multi-timescale Representation Learning in LSTM Language Models

no code implementations ICLR 2021 Shivangi Mahto, Vy A. Vo, Javier S. Turek, Alexander G. Huth

Earlier work has demonstrated that dependencies in natural language tend to decay with distance between words according to a power law.

Language Modeling Language Modelling +1

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