- Home
- Search Results
- Page 1 of 1
Search for: All records
-
Total Resources4
- Resource Type
-
0002000002000000
- More
- Availability
-
31
- Author / Contributor
- Filter by Author / Creator
-
-
Timalsina, Umesh (4)
-
Biswas, Gautam (2)
-
Davalos, Eduardo (2)
-
Fonteles, Joyce Horn (2)
-
Zhang, Yike (2)
-
Acacio Sanchez, Manuel E. (1)
-
Broll, Brian (1)
-
Budavári, Tamás (1)
-
Craven, Nicholas C. (1)
-
Crawford, Brad (1)
-
Cummings, Peter T. (1)
-
Gilmer, Justin B. (1)
-
Lédeczi, Ákos (1)
-
McCabe, Clare (1)
-
Potoff, Jeffrey J. (1)
-
Quach, Co D. (1)
-
S, Ashwin T (1)
-
Völgyesi, Péter (1)
-
Wu, Jiayi (1)
-
#Tyler Phillips, Kenneth E. (0)
-
- Filter by Editor
-
-
& Spizer, S. M. (0)
-
& . Spizer, S. (0)
-
& Ahn, J. (0)
-
& Bateiha, S. (0)
-
& Bosch, N. (0)
-
& Brennan K. (0)
-
& Brennan, K. (0)
-
& Chen, B. (0)
-
& Chen, Bodong (0)
-
& Drown, S. (0)
-
& Ferretti, F. (0)
-
& Higgins, A. (0)
-
& J. Peters (0)
-
& Kali, Y. (0)
-
& Ruiz-Arias, P.M. (0)
-
& S. Spitzer (0)
-
& Sahin. I. (0)
-
& Spitzer, S. (0)
-
& Spitzer, S.M. (0)
-
(submitted - in Review for IEEE ICASSP-2024) (0)
-
-
Have feedback or suggestions for a way to improve these results?
!
Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to non-federal websites. Their policies may differ from this site.
-
Free, publicly-accessible full text available November 4, 2025
-
Davalos, Eduardo; Timalsina, Umesh; Zhang, Yike; Wu, Jiayi; Fonteles, Joyce Horn; Biswas, Gautam (, IEEE)
-
Crawford, Brad; Timalsina, Umesh; Quach, Co D.; Craven, Nicholas C.; Gilmer, Justin B.; McCabe, Clare; Cummings, Peter T.; Potoff, Jeffrey J. (, Journal of Chemical Information and Modeling)
-
Broll, Brian; Timalsina, Umesh; Völgyesi, Péter; Budavári, Tamás; Lédeczi, Ákos; Acacio Sanchez, Manuel E. (, Scientific Programming)The paper introduces DeepForge, a gateway to deep learning for scientific computing. DeepForge provides an easy to use, yet powerful visual/textual interface to facilitate the rapid development of deep learning models by novices as well as experts. Utilizing a cloud-based infrastructure, built-in version control, and multiuser collaboration support, DeepForge promotes reproducibility and ease of access and enables remote execution of machine learning pipelines. The tool currently supports TensorFlow/Keras, but its extensible architecture enables easy integration of additional platforms.more » « less
An official website of the United States government
