Search Results - "Vo, Vy A"

Value-driven attentional capture enhances distractor representations in early visual cortex by Itthipuripat, Sirawaj, Vo, Vy A, Sprague, Thomas C, Serences, John T

“…When a behaviorally relevant stimulus has been previously associated with reward, behavioral responses are faster and more accurate compared to equally…”
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Dissociable signatures of visual salience and behavioral relevance across attentional priority maps in human cortex by Sprague, Thomas C, Itthipuripat, Sirawaj, Vo, Vy A, Serences, John T

“…Computational models posit that visual attention is guided by activity within spatial maps that index the image-computable salience and the behavioral…”
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Spatial Tuning Shifts Increase the Discriminability and Fidelity of Population Codes in Visual Cortex by Vo, Vy A, Sprague, Thomas C, Serences, John T

“…Selective visual attention enables organisms to enhance the representation of behaviorally relevant stimuli by altering the encoding properties of single…”
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Young Children Bet on Their Numerical Skills: Metacognition in the Numerical Domain by Vo, Vy A., Li, Rosa, Kornell, Nate, Pouget, Alexandre, Cantlon, Jessica F.

“…Metacognition, the ability to assess one's own knowledge, has been targeted as a critical learning mechanism in mathematics education. Yet the early childhood…”
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Computational Language Modeling and the Promise of In Silico Experimentation by Jain, Shailee, Vo, Vy A, Wehbe, Leila, Huth, Alexander G

“…Language neuroscience currently relies on two major experimental paradigms: controlled experiments using carefully hand-designed stimuli, and natural stimulus…”
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Shared Representational Formats for Information Maintained in Working Memory and Information Retrieved from Long-Term Memory by Vo, Vy A, Sutterer, David W, Foster, Joshua J, Sprague, Thomas C, Awh, Edward, Serences, John T

“…Abstract Current theories propose that the short-term retention of information in working memory (WM) and the recall of information from long-term memory (LTM)…”
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Inverted Encoding Models Assay Population-Level Stimulus Representations, Not Single-Unit Neural Tuning by Sprague, Thomas C, Adam, Kirsten C S, Foster, Joshua J, Rahmati, Masih, Sutterer, David W, Vo, Vy A

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Memory-Augmented Graph Neural Networks: A Brain-Inspired Review by Ma, Guixiang, Vo, Vy A., Willke, Theodore L., Ahmed, Nesreen K.

“…Graph neural networks (GNNs) have been extensively used for many domains where data are represented as graphs, including social networks, recommender systems,…”
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Memory-Augmented Graph Neural Networks: A Brain-Inspired Review by Ma, Guixiang, Vo, Vy A, Willke, Theodore, Ahmed, Nesreen K

“…We provide a comprehensive review of the existing literature on memory-augmented GNNs. We review these works through the lens of psychology and neuroscience,…”
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OMPar: Automatic Parallelization with AI-Driven Source-to-Source Compilation by Kadosh, Tal, Hasabnis, Niranjan, Soundararajan, Prema, Vo, Vy A, Capota, Mihai, Ahmed, Nesreen, Pinter, Yuval, Oren, Gal

“…Manual parallelization of code remains a significant challenge due to the complexities of modern software systems and the widespread adoption of multi-core…”
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Brain encoding models based on multimodal transformers can transfer across language and vision by Tang, Jerry, Du, Meng, Vo, Vy A, Lal, Vasudev, Huth, Alexander G

“…Encoding models have been used to assess how the human brain represents concepts in language and vision. While language and vision rely on similar concept…”
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Assessing Episodic Memory in LLMs with Sequence Order Recall Tasks by Pink, Mathis, Vo, Vy A, Wu, Qinyuan, Mu, Jianing, Turek, Javier S, Hasson, Uri, Norman, Kenneth A, Michelmann, Sebastian, Huth, Alexander, Toneva, Mariya

“…Current LLM benchmarks focus on evaluating models' memory of facts and semantic relations, primarily assessing semantic aspects of long-term memory. However,…”
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Memory in humans and deep language models: Linking hypotheses for model augmentation by Raccah, Omri, Chen, Phoebe, Willke, Ted L, Poeppel, David, Vo, Vy A

“…The computational complexity of the self-attention mechanism in Transformer models significantly limits their ability to generalize over long temporal…”
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MPIrigen: MPI Code Generation through Domain-Specific Language Models by Schneider, Nadav, Hasabnis, Niranjan, Vo, Vy A, Kadosh, Tal, Krien, Neva, Capotă, Mihai, Tamir, Guy, Willke, Ted, Ahmed, Nesreen, Pinter, Yuval, Mattson, Timothy, Oren, Gal

“…The imperative need to scale computation across numerous nodes highlights the significance of efficient parallel computing, particularly in the realm of…”
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MonoCoder: Domain-Specific Code Language Model for HPC Codes and Tasks by Kadosh, Tal, Hasabnis, Niranjan, Vo, Vy A, Schneider, Nadav, Krien, Neva, Capota, Mihai, Wasay, Abdul, Ahmed, Nesreen, Willke, Ted, Tamir, Guy, Pinter, Yuval, Mattson, Timothy, Oren, Gal

“…With easier access to powerful compute resources, there is a growing trend in AI for software development to develop large language models (LLMs) to address a…”
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Scope is all you need: Transforming LLMs for HPC Code by Kadosh, Tal, Hasabnis, Niranjan, Vo, Vy A, Schneider, Nadav, Krien, Neva, Wasay, Abdul, Ahmed, Nesreen, Willke, Ted, Tamir, Guy, Pinter, Yuval, Mattson, Timothy, Oren, Gal

“…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…”
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The Landscape and Challenges of HPC Research and LLMs by Chen, Le, Ahmed, Nesreen K, Dutta, Akash, Bhattacharjee, Arijit, Yu, Sixing, Mahmud, Quazi Ishtiaque, Abebe, Waqwoya, Phan, Hung, Sarkar, Aishwarya, Butler, Branden, Hasabnis, Niranjan, Oren, Gal, Vo, Vy A, Munoz, Juan Pablo, Willke, Theodore L, Mattson, Tim, Jannesari, Ali

“…Recently, language models (LMs), especially large language models (LLMs), have revolutionized the field of deep learning. Both encoder-decoder models and…”
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Multi-timescale Representation Learning in LSTM Language Models by Mahto, Shivangi, Vo, Vy A, Turek, Javier S, Huth, Alexander G

“…International Conference on Learning Representations 2021 Language models must capture statistical dependencies between words at timescales ranging from very…”
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Slower is Better: Revisiting the Forgetting Mechanism in LSTM for Slower Information Decay by Chien, Hsiang-Yun Sherry, Turek, Javier S, Beckage, Nicole, Vo, Vy A, Honey, Christopher J, Willke, Ted L

“…Sequential information contains short- to long-range dependencies; however, learning long-timescale information has been a challenge for recurrent neural…”
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