Demo Data from EDBT 2026 / reviewers in the wild / expert
David Carter
dblp:38/3543
· DBLP profile ↗
4ranked-venue papers
3as first author
0since 2021 · last 2017
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1 · 1 first-author
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
1 paper |
Reinforcement learning · 50% Question answering and dialogue systems · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Natural language and speech › Question answering and dialogue systems › dialogue modeling
neural conversation model |
0.3 | 1 | 2017 | Batch Policy Gradient Methods for Improving Neural Conversation Models · ICLR (Poster) 2017 |
Machine learning › Reinforcement learning › policy optimization
policy gradient |
0.3 | 1 | 2017 | Batch Policy Gradient Methods for Improving Neural Conversation Models · ICLR (Poster) 2017 |
Methods — techniques the papers use, named apart from their topics
policy gradient · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Batch Policy Gradient Methods for Improving Neural Conversation Models
Kirthevasan Kandasamy, Yoram Bachrach, Ryota Tomioka, Daniel Tarlow, David Carter |
ICLR (Poster) | 5 |
| 2013 | Using groups to support judgmental parameter estimation VISCONS: 'Eyeballing' to capture a quantified group consensus
David Carter, Jonathan D. Moizer, Shaofeng Liu 0001 |
Expert Syst. Appl. | 1 |
| 2001 | Resource-limited sentence boundary detectionabstractWe examine the practical constraints imposed on the task of sentence boundary detection in speech recognizer output, by the requirements of a system that supports large-scale commercial off-line transcription of dictations. We develop and evaluate a method that observes these constraints, reformulating the best technique previously reported in order to allow the use a smoothing technique directly tailored to boundary prediction. We then show how this method can be generalized and improved upon, demonstrating significantly better performance in three different domains. David Carter, Ian Gransden |
INTERSPEECH | 1 |
| 1998 | What Industry Wants in Its New Hires
David Carter, Miriam F. Browning, Robert Carr, Glenn Rosander |
CSEE&T | 1 |