Zobrazeno 1 - 10
of 29 925
pro vyhledávání: '"Gifford, A"'
Human vision is mediated by a complex interconnected network of cortical brain areas jointly representing visual information. While these areas are increasingly understood in isolation, their representational relationships remain elusive. Here we dev
Externí odkaz:
http://arxiv.org/abs/2411.10872
Causal inference provides an analytical framework to identify and quantify cause-and-effect relationships among a network of interacting agents. This paper offers a novel framework for analyzing cascading failures in power transmission networks. This
Externí odkaz:
http://arxiv.org/abs/2410.19179
Autor:
Tawfik, Kareem O., Khan, Mohammad M. R., Patro, Ankita, Smetak, Miriam R., Haynes, David, Labadie, Robert F., Gifford, René H., Noble, Jack H.
Hypothesis: Pre-operative cochlear implant (CI) electrode array (EL) insertion plans created by automated image analysis methods can improve positioning of slim pre-curved EL. Background: This study represents the first evaluation of a system for pat
Externí odkaz:
http://arxiv.org/abs/2410.18366
Autor:
Guo, Manshan, Choksi, Bhavin, Sadiya, Sari, Gifford, Alessandro T., Vilas, Martina G., Cichy, Radoslaw M., Roig, Gemma
In contrast to human vision, artificial neural networks (ANNs) remain relatively susceptible to adversarial attacks. To address this vulnerability, efforts have been made to transfer inductive bias from human brains to ANNs, often by training the ANN
Externí odkaz:
http://arxiv.org/abs/2409.03646
Autor:
Shinkle, Emily, Pachalieva, Aleksandra, Bahl, Riti, Matin, Sakib, Gifford, Brendan, Craven, Galen T., Lubbers, Nicholas
Coarse-graining is a molecular modeling technique in which an atomistic system is represented in a simplified fashion that retains the most significant system features that contribute to a target output, while removing the degrees of freedom that are
Externí odkaz:
http://arxiv.org/abs/2406.12112
Autor:
Dai, Zheng, Gifford, David K
Diffusion models are a class of generative models that generate high-quality samples, but at present it is difficult to characterize how they depend upon their training data. This difficulty raises scientific and regulatory questions, and is a conseq
Externí odkaz:
http://arxiv.org/abs/2406.07908
We propose to learn the time-varying stochastic computational resource usage of software as a graph structured Schr\"odinger bridge problem. In general, learning the computational resource usage from data is challenging because resources such as the
Externí odkaz:
http://arxiv.org/abs/2405.12463
Autor:
Ekambaram, Vijay, Jati, Arindam, Dayama, Pankaj, Mukherjee, Sumanta, Nguyen, Nam H., Gifford, Wesley M., Reddy, Chandra, Kalagnanam, Jayant
Large pre-trained models excel in zero/few-shot learning for language and vision tasks but face challenges in multivariate time series (TS) forecasting due to diverse data characteristics. Consequently, recent research efforts have focused on develop
Externí odkaz:
http://arxiv.org/abs/2401.03955
The solution of the path structured multimarginal Schr\"{o}dinger bridge problem (MSBP) is the most-likely measure-valued trajectory consistent with a sequence of observed probability measures or distributional snapshots. We leverage recent algorithm
Externí odkaz:
http://arxiv.org/abs/2310.00604
Autor:
Samuel I. Garcia, Ashley Jacobson, Gregory P. Moore, Jesse Frank, Wyatt Gifford, Samantha Johnson, Donell Lazaro-Paulina, Aidan Mullan, Alexander S. Finch
Publikováno v:
International Journal of Emergency Medicine, Vol 17, Iss 1, Pp 1-4 (2024)
Abstract Introduction In emergency medicine, triage encompasses more than the initial prioritization of treatment; it also includes decisions about the most suitable level of care and disposition for each patient. However, the increasing use of mobil
Externí odkaz:
https://doaj.org/article/c69d381d60244a31a144ba483b2bd824
