Zobrazeno 1 - 10
of 7 888
pro vyhledávání: '"BURNS, JOHN A."'
Autor:
Bouland, Ali, Niu, Shengyuan, Paruchuri, Sai Tej, Kurdila, Andrew, Burns, John, Schuster, Eugenio
This paper studies convergence rates for some value function approximations that arise in a collection of reproducing kernel Hilbert spaces (RKHS) $H(\Omega)$. By casting an optimal control problem in a specific class of native spaces, strong rates o
Externí odkaz:
http://arxiv.org/abs/2309.07383
Autor:
Kane, Haunani H., Choy, C. Anela, Bruno, Barbara C., Tachera, Diamond K., Keliipuleole, Keku‘iapōiula, Wong-Ala, Jennifer A.T.K., Burns, John H.R., Kapono, Clifford A., Pascoe, Kailey H., Steward, Kainalu, Alegado, Rosanna ‘Anolani
Publikováno v:
Oceanography, 2023 Dec 01. 36(4), 35-43.
Externí odkaz:
https://www.jstor.org/stable/27278249
Publikováno v:
Oceanography, 2023 Dec 01. 36(4), 146-147.
Externí odkaz:
https://www.jstor.org/stable/27278277
This paper derives error bounds for regression in continuous time over subsets of certain types of Riemannian manifolds.The regression problem is typically driven by a nonlinear evolution law taking values on the manifold, and it is cast as one of op
Externí odkaz:
http://arxiv.org/abs/2209.03804
Autor:
Burns, John Edward, Jollands, Stephen
Publikováno v:
Accounting, Auditing & Accountability Journal, 2022, Vol. 37, Issue 2, pp. 627-637.
Externí odkaz:
http://www.emeraldinsight.com/doi/10.1108/AAAJ-05-2022-5811
Deep learning approaches applied to medical imaging have reached near-human or better-than-human performance on many diagnostic tasks. For instance, the CheXpert competition on detecting pathologies in chest x-rays has shown excellent multi-class cla
Externí odkaz:
http://arxiv.org/abs/2204.07824
Autor:
Banerjee, Imon, Bhimireddy, Ananth Reddy, Burns, John L., Celi, Leo Anthony, Chen, Li-Ching, Correa, Ramon, Dullerud, Natalie, Ghassemi, Marzyeh, Huang, Shih-Cheng, Kuo, Po-Chih, Lungren, Matthew P, Palmer, Lyle, Price, Brandon J, Purkayastha, Saptarshi, Pyrros, Ayis, Oakden-Rayner, Luke, Okechukwu, Chima, Seyyed-Kalantari, Laleh, Trivedi, Hari, Wang, Ryan, Zaiman, Zachary, Zhang, Haoran, Gichoya, Judy W
Background: In medical imaging, prior studies have demonstrated disparate AI performance by race, yet there is no known correlation for race on medical imaging that would be obvious to the human expert interpreting the images. Methods: Using private
Externí odkaz:
http://arxiv.org/abs/2107.10356
Autor:
Burns, John W., Jensen, Mark P., Thorn, Beverly E., Lillis, Teresa A., Carmody, James, Gerhart, James, Keefe, Francis
Publikováno v:
In The Journal of Pain June 2024 25(6)
Autor:
Sun, Ju, Peng, Le, Li, Taihui, Adila, Dyah, Zaiman, Zach, Melton, Genevieve B., Ingraham, Nicholas, Murray, Eric, Boley, Daniel, Switzer, Sean, Burns, John L., Huang, Kun, Allen, Tadashi, Steenburg, Scott D., Gichoya, Judy Wawira, Kummerfeld, Erich, Tignanelli, Christopher
Importance: An artificial intelligence (AI)-based model to predict COVID-19 likelihood from chest x-ray (CXR) findings can serve as an important adjunct to accelerate immediate clinical decision making and improve clinical decision making. Despite si
Externí odkaz:
http://arxiv.org/abs/2106.02118