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» A learning model for oscillatory networks
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ENGL
2007
89views more  ENGL 2007»
15 years 6 months ago
Similarity-based Heterogeneous Neural Networks
This research introduces a general class of functions serving as generalized neuron models to be used in artificial neural networks. They are cast in the common framework of comp...
Lluís A. Belanche Muñoz, Julio Jose ...
IUI
2004
ACM
15 years 12 months ago
Sheepdog: learning procedures for technical support
Technical support procedures are typically very complex. Users often have trouble following printed instructions describing how to perform these procedures, and these instructions...
Tessa A. Lau, Lawrence D. Bergman, Vittorio Castel...
NECO
2010
103views more  NECO 2010»
15 years 4 months ago
Posterior Weighted Reinforcement Learning with State Uncertainty
Reinforcement learning models generally assume that a stimulus is presented that allows a learner to unambiguously identify the state of nature, and the reward received is drawn f...
Tobias Larsen, David S. Leslie, Edmund J. Collins,...
NIPS
2004
15 years 7 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
PPOPP
2009
ACM
16 years 7 months ago
Mapping parallelism to multi-cores: a machine learning based approach
The efficient mapping of program parallelism to multi-core processors is highly dependent on the underlying architecture. This paper proposes a portable and automatic compiler-bas...
Zheng Wang, Michael F. P. O'Boyle