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Hugh Perkins

dblp:136/5700 · DBLP profile ↗
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3ranked-venue papers
1as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 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
3 papers
Probabilistic and Bayesian machine learning · 51% Information extraction and text analysis · 49%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › monte carlo methods › markov chain monte carlo
gibbs sampling		 0.422014
Gibbs max-margin topic models with data augmentation · J. Mach. Learn. Res. 2014
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms · ICML (1) 2013
Natural language and speech › Information extraction and text analysis
topic model		 0.422014
Gibbs max-margin topic models with data augmentation · J. Mach. Learn. Res. 2014
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms · ICML (1) 2013
Machine learning › Probabilistic and Bayesian machine learning › statistical inference
bayesian inference		 0.212014
Gibbs max-margin topic models with data augmentation · J. Mach. Learn. Res. 2014
Natural language and speech › Information extraction and text analysis › topic model
supervised topic model		 0.212013
Gibbs Max-Margin Topic Models with Fast Sampling Algorithms · ICML (1) 2013
Data mining
clustering		 0.112019
Dialog Intent Induction with Deep Multi-View Clustering · EMNLP/IJCNLP (1) 2019
Data mining › clustering
multi-view clustering		 0.112019
Dialog Intent Induction with Deep Multi-View Clustering · EMNLP/IJCNLP (1) 2019

Methods — techniques the papers use, named apart from their topics

deep clustering · 0.8max-margin learning · 0.4data augmentation · 0.2gibbs sampling · 0.2augmented variables · 0.2
YearPublicationVenuePosition
2019 Dialog Intent Induction with Deep Multi-View Clustering
abstract
Hugh Perkins, Yi Yang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Hugh Perkins, Yi Yang 0038
 EMNLP/IJCNLP (1)1
2014 Gibbs max-margin topic models with data augmentation
Jun Zhu 0001, Ning Chen 0002, Hugh Perkins, Bo Zhang 0010
 J. Mach. Learn. Res.3
2013 Gibbs Max-Margin Topic Models with Fast Sampling Algorithms
abstract
Existing max-margin supervised topic models rely on an iterative procedure to solve multiple latent SVM subproblems with additional mean-field assumptions on the desired posterior distributions. This paper presents Gibbs max-margin supervised topic models by minimizing an expected margin loss, an upper bound of the existing margin loss derived from an expected prediction rule. By introducing augmented variables, we develop simple and fast Gibbs sampling algorithms with no restricting assumptions and no need to solve SVM subproblems for both classification and regression. Empirical results demonstrate significant improvements on time efficiency. The classification performance is also significantly improved over competitors.
Jun Zhu 0001, Ning Chen 0002, Hugh Perkins, Bo Zhang 0010
 ICML (1)3