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» Learning about and through Empirical Modelling
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JMLR
2012
13 years 4 days ago
Structured Output Learning with High Order Loss Functions
Often when modeling structured domains, it is desirable to leverage information that is not naturally expressed as simply a label. Examples include knowledge about the evaluation ...
Daniel Tarlow, Richard S. Zemel
SIGIR
2000
ACM
15 years 2 months ago
Bridging the lexical chasm: statistical approaches to answer-finding
Abstract This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The lea...
Adam L. Berger, Rich Caruana, David Cohn, Dayne Fr...
JMLR
2010
106views more  JMLR 2010»
14 years 4 months ago
Why Does Unsupervised Pre-training Help Deep Learning?
Much recent research has been devoted to learning algorithms for deep architectures such as Deep Belief Networks and stacks of auto-encoder variants, with impressive results obtai...
Dumitru Erhan, Yoshua Bengio, Aaron C. Courville, ...
FGR
2004
IEEE
230views Biometrics» more  FGR 2004»
15 years 1 months ago
Tracking Humans using Prior and Learned Representations of Shape and Appearance
Tracking a moving person is challenging because a person's appearance in images changes significantly due to articulation, viewpoint changes, and lighting variation across a ...
Jongwoo Lim, David J. Kriegman
COGSR
2011
109views more  COGSR 2011»
14 years 4 months ago
How groups develop a specialized domain vocabulary: A cognitive multi-agent model
We simulate the evolution of a domain vocabulary in small communities. Empirical data show that human communicators can evolve graphical languages quickly in a constrained task (P...
David Reitter, Christian Lebiere