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David Carter

dblp:38/3543 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Natural language and speech › Question answering and dialogue systems › dialogue modeling
neural conversation model
0.312017
Batch Policy Gradient Methods for Improving Neural Conversation Models · ICLR (Poster) 2017
Machine learning › Reinforcement learning › policy optimization
policy gradient
0.312017
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
YearPublicationVenuePosition
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 detection
abstract
We 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
INTERSPEECH1
1998 What Industry Wants in Its New Hires
David Carter, Miriam F. Browning, Robert Carr, Glenn Rosander
CSEE&T1