Zobrazeno 1 - 10
of 10 684
pro vyhledávání: '"Vann, A."'
Publikováno v:
Proceedings of the ICML 2024 Workshop on Structured Probabilistic Inference & Generative Modeling
Knowledge graphs (KGs) are crucial for representing and reasoning over structured information, supporting a wide range of applications such as information retrieval, question answering, and decision-making. However, their effectiveness is often hinde
Externí odkaz:
http://arxiv.org/abs/2412.08742
Autor:
Chouldechova, Alexandra, Atalla, Chad, Barocas, Solon, Cooper, A. Feder, Corvi, Emily, Dow, P. Alex, Garcia-Gathright, Jean, Pangakis, Nicholas, Reed, Stefanie, Sheng, Emily, Vann, Dan, Vogel, Matthew, Washington, Hannah, Wallach, Hanna
The valid measurement of generative AI (GenAI) systems' capabilities, risks, and impacts forms the bedrock of our ability to evaluate these systems. We introduce a shared standard for valid measurement that helps place many of the disparate-seeming e
Externí odkaz:
http://arxiv.org/abs/2412.01934
Autor:
Wallach, Hanna, Desai, Meera, Pangakis, Nicholas, Cooper, A. Feder, Wang, Angelina, Barocas, Solon, Chouldechova, Alexandra, Atalla, Chad, Blodgett, Su Lin, Corvi, Emily, Dow, P. Alex, Garcia-Gathright, Jean, Olteanu, Alexandra, Reed, Stefanie, Sheng, Emily, Vann, Dan, Vaughan, Jennifer Wortman, Vogel, Matthew, Washington, Hannah, Jacobs, Abigail Z.
Across academia, industry, and government, there is an increasing awareness that the measurement tasks involved in evaluating generative AI (GenAI) systems are especially difficult. We argue that these measurement tasks are highly reminiscent of meas
Externí odkaz:
http://arxiv.org/abs/2411.10939
Autor:
Speirs, David C., Ruiz-Ruiz, Juan, Giacomin, Maurizio, Hall-Chen, Valerian H., Phelps, Alan D. R., Vann, Roddy, Huggard, Peter G., Wang, Hui, Field, Anthony, Ronald, Kevin
Plasma turbulence on disparate spatial and temporal scales plays a key role in defining the level of confinement achievable in tokamaks, with the development of reduced numerical models for cross-scale turbulence effects informed by experimental meas
Externí odkaz:
http://arxiv.org/abs/2408.15807
Autor:
Garg, Deepeka, Evans, Benjamin Patrick, Ardon, Leo, Narayanan, Annapoorani Lakshmi, Vann, Jared, Madhushani, Udari, Henry-Nickie, Makada, Ganesh, Sumitra
Mortgages account for the largest portion of household debt in the United States, totaling around \$12 trillion nationwide. In times of financial hardship, alleviating mortgage burdens is essential for supporting affected households. The mortgage ser
Externí odkaz:
http://arxiv.org/abs/2402.17932
Objective: This study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. Background: Latency between command,
Externí odkaz:
http://arxiv.org/abs/2311.09062
As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, the challenge of operational delays remains a significant obstacle. These delays are inherent in signal transmission and proc
Externí odkaz:
http://arxiv.org/abs/2311.08255
Autor:
Micheline Lagacé, Saeed Montazeri, Daphne Kamino, Eva Mamak, Linh G. Ly, Cecil D. Hahn, Vann Chau, Sampsa Vanhatalo, Emily W. Y. Tam
Publikováno v:
Annals of Clinical and Translational Neurology, Vol 11, Iss 12, Pp 3267-3279 (2024)
Abstract Objective Assess the capacity of brain state of the newborn (BSN) to predict neurodevelopment outcomes in neonatal encephalopathy. Methods Trends of BSN, a deep learning‐based measure translating EEG background to a continuous trend, were
Externí odkaz:
https://doaj.org/article/2d794b31ef0c4c699ad9c39ebbbb98e3
Autor:
Magooda, Ahmed, Helyar, Alec, Jackson, Kyle, Sullivan, David, Atalla, Chad, Sheng, Emily, Vann, Dan, Edgar, Richard, Palangi, Hamid, Lutz, Roman, Kong, Hongliang, Yun, Vincent, Kamal, Eslam, Zarfati, Federico, Wallach, Hanna, Bird, Sarah, Chen, Mei
We present a framework for the automated measurement of responsible AI (RAI) metrics for large language models (LLMs) and associated products and services. Our framework for automatically measuring harms from LLMs builds on existing technical and soc
Externí odkaz:
http://arxiv.org/abs/2310.17750
Autor:
Xiao, Yuchen, Sun, Yanchao, Xu, Mengda, Madhushani, Udari, Vann, Jared, Garg, Deepeka, Ganesh, Sumitra
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems. By imitating few-shot examples provided in the prompts (i.e., in-context learning), an LLM agent can interact wit
Externí odkaz:
http://arxiv.org/abs/2310.14403