Demo Data from EDBT 2026 / reviewers in the wild / expert
David C. Uthus
dblp:09/2971
· DBLP profile ↗
7ranked-venue papers
4as first author
3since 2021 · last 2024
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 7 · 4 first-author · 3 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
3 papers |
Language models and text generation · 45% Deep learning architectures and training · 42% Transfer learning and domain adaptation · 10% | |
| Computer graphics and multimedia
1 paper |
Multimedia analysis and retrieval · 100% |
Topics — the 5 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › transformer
efficient transformer |
0.7 | 1 | 2023 | CoLT5: Faster Long-Range Transformers with Conditional Computation · EMNLP 2023 |
Natural language and speech › Language models and text generation › controllable text generation
text style transfer |
0.5 | 1 | 2021 | TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling · ACL/IJCNLP (1) 2021 |
Natural language and speech › Language models and text generation › language modeling
long-context language modeling |
0.2 | 1 | 2023 | CoLT5: Faster Long-Range Transformers with Conditional Computation · EMNLP 2023 |
Machine learning › Transfer learning and domain adaptation
few-shot learning |
0.1 | 1 | 2021 | TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling · ACL/IJCNLP (1) 2021 |
Natural language and speech › Information extraction and text analysis › dialogue analysis
conversational text analysis |
0.0 | 1 | 2013 | Multiparticipant chat analysis: A survey · Artif. Intell. 2013 |
Methods — techniques the papers use, named apart from their topics
sparse attention · 0.7conditional computation · 0.7few-shot learning · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Memory Augmented Language Models through Mixture of Word ExpertsabstractCicero Nogueira dos Santos, James Lee-Thorp, Isaac Noble, Chung-Ching Chang, David Uthus. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. Cícero Nogueira dos Santos, James Lee-Thorp, Isaac Noble, Chung-Ching Chang, David C. Uthus |
NAACL-HLT | 5 |
| 2023 | CoLT5: Faster Long-Range Transformers with Conditional ComputationabstractJoshua Ainslie, Tao Lei, Michiel de Jong, Santiago Ontanon, Siddhartha Brahma, Yury Zemlyanskiy, David Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. Joshua Ainslie, Tao Lei 0001, Michiel de Jong, Santiago Ontañón, Siddhartha Brahma, Yury Zemlyanskiy, David C. Uthus, Mandy Guo, James Lee-Thorp, Yi Tay, Yun-Hsuan Sung, Sumit Sanghai |
EMNLP | 7 |
| 2021 | TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted RestylingabstractParker Riley, Noah Constant, Mandy Guo, Girish Kumar, David Uthus, Zarana Parekh. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021. Parker Riley, Noah Constant, Mandy Guo, David C. Uthus, Zarana Parekh |
ACL/IJCNLP (1) | 5 |
| 2013 | Detecting Bot-Answerable Questions in Ubuntu Chat
David C. Uthus, David W. Aha |
IJCNLP | 1 |
| 2013 | Multiparticipant chat analysis: A survey
David C. Uthus, David W. Aha |
Artif. Intell. | 1 |
| 2009 | DFS* and the Traveling Tournament Problem
David C. Uthus, Patricia J. Riddle, Hans W. Guesgen |
CPAIOR | 1 |
| 2009 | An ant colony optimization approach to the traveling tournament problemabstractThe traveling tournament problem has proven to be a difficult problem for the ant colony optimization metaheuristic, with past approaches showing poor results. This is due to the unusual problem structure and feasibility constraints. We present a new ant colony optimization approach to this problem, hybridizing it with a forward checking and conflict-directed backjumping algorithm while using pattern matching and other constraint satisfaction strategies. The approach improves on the performance of past ant colony optimization approaches, finding better quality solutions in shorter time, and exhibits results comparable to other state-of-the-art approaches. David C. Uthus, Patricia J. Riddle, Hans W. Guesgen |
GECCO | 1 |