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ETS
2002
IEEE
132views Hardware» more  ETS 2002»
13 years 10 months ago
Theories for Deep Change in Affect-sensitive Cognitive Machines: A Constructivist Model
There is interplay between emotions and learning, but this interaction is far more complex than previous learning theories have articulated--this interplay interacts with other re...
Barry Kort, Rob Reilly
ICMLA
2009
13 years 8 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
ICML
2010
IEEE
14 years 19 hour ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
ICML
2009
IEEE
14 years 11 months ago
Deep transfer via second-order Markov logic
Standard inductive learning requires that training and test instances come from the same distribution. Transfer learning seeks to remove this restriction. In shallow transfer, tes...
Jesse Davis, Pedro Domingos
JMLR
2010
106views more  JMLR 2010»
13 years 5 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, ...