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Dmitry Kamenetsky

dblp:16/6850 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2008
—ORCID · none

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

Artificial intelligence and machine learning · 1

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
Probabilistic and Bayesian machine learning · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › probabilistic inference
exact inference
0.112008
Efficient Exact Inference in Planar Ising Models · NIPS 2008
Machine learning › Probabilistic and Bayesian machine learning › structured models
graphical models
0.112008
Efficient Exact Inference in Planar Ising Models · NIPS 2008

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

perfect matching · 0.1dual graph · 0.1
YearPublicationVenuePosition
2008 Efficient Exact Inference in Planar Ising Models
abstract
We present polynomial-time algorithms for the exact computation of lowest- energy states, worst margin violators, partition functions, and marginals in binary undirected graphical models. Our approach provides an interesting alternative to the well-known graph cut paradigm in that it does not impose any submodularity constraints; instead we require planarity to establish a correspondence with perfect matchings in an expanded dual graph. Maximum-margin parameter estimation for a boundary detection task shows our approach to be efficient and effective.
Nicol N. Schraudolph, Dmitry Kamenetsky
NIPS2