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» Strong Separation of Learning Classes
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ML
1998
ACM
14 years 11 months ago
On Restricted-Focus-of-Attention Learnability of Boolean Functions
In the k-Restricted-Focus-of-Attention (k-RFA) model, only k of the n attributes of each example are revealed to the learner, although the set of visible attributes in each example...
Andreas Birkendorf, Eli Dichterman, Jeffrey C. Jac...
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
16 years 6 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
COLT
2008
Springer
15 years 1 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
APPROX
2010
Springer
148views Algorithms» more  APPROX 2010»
15 years 1 months ago
Learning and Lower Bounds for AC0 with Threshold Gates
In 2002 Jackson et al. [JKS02] asked whether AC0 circuits augmented with a threshold gate at the output can be efficiently learned from uniform random examples. We answer this ques...
Parikshit Gopalan, Rocco A. Servedio
CVPR
2011
IEEE
14 years 7 months ago
Learning Message-Passing Inference Machines for Structured Prediction
Nearly every structured prediction problem in computer vision requires approximate inference due to large and complex dependencies among output labels. While graphical models prov...
Stephane Ross, Daniel Munoz, J. Andrew Bagnell