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» Are Hopfield Networks Faster than Conventional Computers
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192
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JMLR
2012
13 years 8 months ago
Krylov Subspace Descent for Deep Learning
In this paper, we propose a second order optimization method to learn models where both the dimensionality of the parameter space and the number of training samples is high. In ou...
Oriol Vinyals, Daniel Povey
151
Voted
IPPS
2009
IEEE
16 years 23 days ago
Dynamic iterations for the solution of ordinary differential equations on multicore processors
In the past few years, there has been a trend of providing increased computing power through greater number of cores on a chip, rather than through higher clock speeds. In order t...
Yanan Yu, Ashok Srinivasan
PERCOM
2005
ACM
16 years 5 months ago
Performance and Energy Efficiency of Block Ciphers in Personal Digital Assistants
Encryption algorithms can be used to help secure wireless communications, but securing data also consumes resources. The goal of this research is to provide users or system develo...
Creighton T. R. Hager, Scott F. Midkiff, Jung Min ...
ANNPR
2006
Springer
15 years 10 months ago
Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques
Abstract. Decomposition techniques are used to speed up training support vector machines but for linear programming support vector machines (LP-SVMs) direct implementation of decom...
Yusuke Torii, Shigeo Abe
171
Voted
DCC
2004
IEEE
16 years 5 months ago
On the Complexity of Rate-Distortion Optimal Streaming of Packetized Media
We consider the problem of rate-distortion optimal streaming of packetized media with sender-driven transmission over a single-QoS network using feedback and retransmissions. For ...
Martin Röder, Jean Cardinal, Raouf Hamzaoui