- 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
-
-
Wang, Zijie J. (3)
-
Chau, Duen Horng (2)
-
Das, Nilaksh (2)
-
Firstman, Robert (2)
-
Hohman, Fred (2)
-
Park, Haekyu (2)
-
Rogers, Emily (2)
-
Abid, Abubakar (1)
-
Agarwal, Akshat (1)
-
Agha, Omar (1)
-
Alabi, Jesujoba (1)
-
Ali, Tariq (1)
-
Alipoormolabashi, Pegah (1)
-
Aminnaseri, Moin (1)
-
Anand, Sajant (1)
-
Andreassen, Anders Johan (1)
-
Arakawa, Riku (1)
-
Argueta, Cedrick (1)
-
Arnaud, Melody (1)
-
Asaadi, Shima (1)
-
- 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.
-
Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present TRANSFORMER EXPLAINER, an interactive visualization tool designed for non-experts to learn about Transformers through the GPT-2 model. Our tool helps users understand complex Transformer concepts by integrating a model overview and smooth transitions across abstraction levels of math operations and model structures. It runs a live GPT-2 model locally in the user’s browser, empowering users to experiment with their own input and observe in real-time how the internal components and parameters of the Transformer work together to predict the next tokens. 125,000 users have used our open-source tool at https://poloclub.github.io/ transformer-explainer/.more » « lessFree, publicly-accessible full text available April 11, 2026
-
Das, Nilaksh; Park, Haekyu; Wang, Zijie J.; Hohman, Fred; Firstman, Robert; Rogers, Emily; Chau, Duen Horng (, IEEE Visualization Conference (VIS))
-
Das, Nilaksh; Park, Haekyu; Wang, Zijie J.; Hohman, Fred; Firstman, Robert; Rogers, Emily; Chau, Duen Horng (, Conference on Human Factors in Computing Systems)
-
Srivastava, Aarohi; Rastogi, Abhinav; Rao, Abhishek; Shoeb, Abu Awal; Abid, Abubakar; Fisch, Adam; Brown, Adam R.; Santoro, Adam; Gupta, Aditya; Garriga-Alonso, Adri; et al (, Transactions on machine learning research)
An official website of the United States government

Full Text Available