Zobrazeno 1 - 10
of 1 846
pro vyhledávání: '"Noseworthy, P."'
The scarcity of labeled action data poses a considerable challenge for developing machine learning algorithms for robotic object manipulation. It is expensive and often infeasible for a robot to interact with many objects. Conversely, visual data of
Externí odkaz:
http://arxiv.org/abs/2412.00145
Autor:
Noseworthy, Michael, Tang, Bingjie, Wen, Bowen, Handa, Ankur, Roy, Nicholas, Fox, Dieter, Ramos, Fabio, Narang, Yashraj, Akinola, Iretiayo
We present FORGE, a method that enables sim-to-real transfer of contact-rich manipulation policies in the presence of significant pose uncertainty. FORGE combines a force threshold mechanism with a dynamics randomization scheme during policy learning
Externí odkaz:
http://arxiv.org/abs/2408.04587
We selected 29 medical imaging projects from 48 candidates, assessed 10 software qualities by answering 108 questions for each software project, and interviewed 8 of the 29 development teams. Based on the quantitative data, we ranked the MI software
Externí odkaz:
http://arxiv.org/abs/2405.12171
Autor:
Gothoskar, Nishad, Ghavami, Matin, Li, Eric, Curtis, Aidan, Noseworthy, Michael, Chung, Karen, Patton, Brian, Freeman, William T., Tenenbaum, Joshua B., Klukas, Mirko, Mansinghka, Vikash K.
Robots cannot yet match humans' ability to rapidly learn the shapes of novel 3D objects and recognize them robustly despite clutter and occlusion. We present Bayes3D, an uncertainty-aware perception system for structured 3D scenes, that reports accur
Externí odkaz:
http://arxiv.org/abs/2312.08715
Autor:
Viengneesee Thao, PhD, MS, Ye Zhu, MD, MPH, PhD, Andrew S. Tseng, MD, MPH, Jonathan W. Inselman, MS, Bijan J. Borah, PhD, Rozalina G. McCoy, MD, MS, Zachi I. Attia, PhD, Francisco Lopez-Jimenez, MD, MBA, Patricia A. Pellikka, MD, David R. Rushlow, MD, MBOE, Paul A. Friedman, MD, Peter A. Noseworthy, MD, MBA, Xiaoxi Yao, MPH, MS, PhD
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 4, Pp 620-631 (2024)
Objective: To investigate the cost-effectiveness of using artificial intelligence (AI) to screen for low ejection fraction (EF) in routine clinical practice using a pragmatic randomized controlled trial (RCT). Patients and Methods: In a post hoc anal
Externí odkaz:
https://doaj.org/article/1dd20220020147a9b981c791a54a4ceb
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 4, Pp 499-510 (2024)
The emergence of artificial intelligence (AI) and other digital solutions in health care has considerably altered the landscape of medical research and patient care. Rigorous evaluation in routine practice settings is fundamental to the ethical use o
Externí odkaz:
https://doaj.org/article/d695d054ccb5431fa8e1bcd4391e123d
Autor:
Levi W. Disrud, Tara A. Gosse, MS, Zach D. Linn, MS, Anthony H. Kashou, MD, Peter A. Noseworthy, MD, MBA, Angela Fink, MSN, Dawn Griffin, MA, MBA, Blade Faust
Publikováno v:
Mayo Clinic Proceedings: Digital Health, Vol 2, Iss 4, Pp 542-547 (2024)
Objective: To investigate the operational outcomes and implementation effects of tiered cardiac telemetry monitoring in a hospital environment using an innovative technology. Patients and Methods: The research focuses on assessing the precision, spee
Externí odkaz:
https://doaj.org/article/2eeb52a8d0054dd0a2a04e1e15254f9a
In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing. This representation can be used to infer door-related parameters and predict the outcomes of int
Externí odkaz:
http://arxiv.org/abs/2305.16567
Autor:
Donnchadh O’Sullivan, Scott Anjewierden, Grace Greason, Itzhak Zachi Attia, Francisco Lopez-Jimenez, Paul A. Friedman, Peter Noseworthy, Jason Anderson, Anthony Kashou, Samuel J. Asirvatham, Benjamin W. Eidem, Jonathan N. Johnson, Talha Niaz, Malini Madhavan
Publikováno v:
npj Digital Medicine, Vol 7, Iss 1, Pp 1-5 (2024)
Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence
Externí odkaz:
https://doaj.org/article/68531a39abd54b56b82e3c0a5e7e6fc7
Autor:
QueerInAI, Organizers Of, Ovalle, Anaelia, Subramonian, Arjun, Singh, Ashwin, Voelcker, Claas, Sutherland, Danica J., Locatelli, Davide, Breznik, Eva, Klubička, Filip, Yuan, Hang, J, Hetvi, Zhang, Huan, Shriram, Jaidev, Lehman, Kruno, Soldaini, Luca, Sap, Maarten, Deisenroth, Marc Peter, Pacheco, Maria Leonor, Ryskina, Maria, Mundt, Martin, Agarwal, Milind, McLean, Nyx, Xu, Pan, Pranav, A, Korpan, Raj, Ray, Ruchira, Mathew, Sarah, Arora, Sarthak, John, ST, Anand, Tanvi, Agrawal, Vishakha, Agnew, William, Long, Yanan, Wang, Zijie J., Talat, Zeerak, Ghosh, Avijit, Dennler, Nathaniel, Noseworthy, Michael, Jha, Sharvani, Baylor, Emi, Joshi, Aditya, Bilenko, Natalia Y., McNamara, Andrew, Gontijo-Lopes, Raphael, Markham, Alex, Dǒng, Evyn, Kay, Jackie, Saraswat, Manu, Vytla, Nikhil, Stark, Luke
Publikováno v:
2023 ACM Conference on Fairness, Accountability, and Transparency
We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerg
Externí odkaz:
http://arxiv.org/abs/2303.16972