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ICCV
2011
IEEE
12 years 7 months ago
Adaptive Deconvolutional Networks for Mid and High Level Feature Learning
We present a hierarchical model that learns image decompositions via alternating layers of convolutional sparse coding and max pooling. When trained on natural images, the layers ...
Matthew D. Zeiler, Graham W. Taylor, Rob Fergus
CSC
2006
13 years 9 months ago
An Adaptive Method for Flow Simulation in Three-Dimensional Heterogeneous Discrete Fracture Networks
Natural fractured media are highly unpredictable because of existing complex structures at the fracture and at the network levels. Fractures are by themselves heterogeneous objects...
Hussein Mustapha
GECCO
2007
Springer
182views Optimization» more  GECCO 2007»
13 years 11 months ago
Stochastic training of a biologically plausible spino-neuromuscular system model
A primary goal of evolutionary robotics is to create systems that are as robust and adaptive as the human body. Moving toward this goal often involves training control systems tha...
Stanley Phillips Gotshall, Terence Soule
MSWIM
2006
ACM
14 years 1 months ago
Gateway adaptive pacing for TCP across multihop wireless networks and the Internet
In this paper, we introduce an effective congestion control scheme for TCP over hybrid wireless/wired networks comprising a multihop wireless IEEE 802.11 network and the wired Int...
Sherif M. ElRakabawy, Alexander Klemm, Christoph L...
CORR
2010
Springer
167views Education» more  CORR 2010»
13 years 7 months ago
Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured sparsityinducing norm defined as the sum of -norms over groups of variables. Whereas a lot of effort has been pu...
Julien Mairal, Rodolphe Jenatton, Guillaume Obozin...