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» Modeling Classification and Inference Learning
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ACL
2010
15 years 6 days ago
Bootstrapping Semantic Analyzers from Non-Contradictory Texts
We argue that groups of unannotated texts with overlapping and non-contradictory semantics represent a valuable source of information for learning semantic representations. A simp...
Ivan Titov, Mikhail Kozhevnikov
141
Voted
JMLR
2010
143views more  JMLR 2010»
14 years 9 months ago
Beware of the DAG!
Directed acyclic graph (DAG) models are popular tools for describing causal relationships and for guiding attempts to learn them from data. In particular, they appear to supply a ...
A. Philip Dawid
ICASSP
2011
IEEE
14 years 5 months ago
Cooperative prey herding based on diffusion adaptation
Mobile adaptive networks consist of a collection of nodes with learning and motion abilities that interact with each other locally in order to solve distributed processing and dis...
Sheng-Yuan Tu, Ali H. Sayed
173
Voted
DATAMINE
2010
161views more  DATAMINE 2010»
14 years 11 months ago
Predicting labels for dyadic data
: In dyadic prediction, the input consists of a pair of items (a dyad), and the goal is to predict the value of an observation related to the dyad. Special cases of dyadic predicti...
Aditya Krishna Menon, Charles Elkan
ICS
2005
Tsinghua U.
15 years 7 months ago
What is worth learning from parallel workloads?: a user and session based analysis
Learning useful and predictable features from past workloads and exploiting them well is a major source of improvement in many operating system problems. We review known parallel ...
Julia Zilber, Ofer Amit, David Talby