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» On the Execution of Deep Models
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CVPR
2011
IEEE
13 years 2 months ago
On Deep Generative Models with Applications to Recognition
The most popular way to use probabilistic models in vision is first to extract some descriptors of small image patches or object parts using well-engineered features, and then to...
Marc', Aurelio Ranzato, Joshua Susskind, Volodymyr...
DATE
2008
IEEE
204views Hardware» more  DATE 2008»
14 years 1 months ago
Deep Submicron Interconnect Timing Model with Quadratic Random Variable Analysis
Shrinking feature sizes and process variations are of increasing concern in modern technology. It is urgent that we develop statistical interconnect timing models which are harmon...
Jun-Kuei Zeng, Chung-Ping Chen
ASPLOS
2012
ACM
12 years 2 months ago
DreamWeaver: architectural support for deep sleep
Numerous data center services exhibit low average utilization leading to poor energy efficiency. Although CPU voltage and frequency scaling historically has been an effective mea...
David Meisner, Thomas F. Wenisch
ETS
2002
IEEE
132views Hardware» more  ETS 2002»
13 years 6 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
ISCAS
2005
IEEE
136views Hardware» more  ISCAS 2005»
14 years 8 days ago
Probabilistic computing with future deep sub-micrometer devices: a modelling approach
Nor H. Hamid, Alan F. Murray, David I. Laurenson, ...