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Contextual Influences on Saliency
(2004-04-14)
This article describes a model for including scene/context priors in attention guidance. In the proposed scheme, visual context information can be available early in the visual processing chain, in order to modulate the ...
A Constant-Factor Approximation Algorithm for Embedding Unweighted Graphs into Trees
(2004-07-05)
We present a constant-factor approximation algorithm for computing an embedding of the shortest path metric of an unweighted graph into a tree, that minimizes the multiplicative distortion.
Optimal Approximations of the Frequency Moments
(2004-07-02)
We give a one-pass, O~(m^{1-2/k})-space algorithm for estimating the k-th frequency moment of a data stream for any real k>2. Together with known lower bounds, this resolves the main problem left open by Alon, Matias, ...
Contextual Influences on Saliency
(2004-04-14)
This article describes a model for including scene/context priors in attention guidance. In the proposed scheme, visual context information can be available early in the visual processing chain, in order to modulate the ...
Cascading Regularized Classifiers
(2004-04-21)
Among the various methods to combine classifiers, Boosting was originally thought as an stratagem to cascade pairs of classifiers through their disagreement. I recover the same idea from the work of Niyogi et al. to show ...
Methods and Experiments With Bounded Tree-width Markov Networks
(2004-12-30)
Markov trees generalize naturally to bounded tree-width Markov networks, onwhich exact computations can still be done efficiently. However, learning themaximum likelihood Markov network with tree-width greater than 1 is ...
A general mechanism for tuning: Gain control circuits and synapses underlie tuning of cortical neurons
(2004-12-31)
Tuning to an optimal stimulus is a widespread property of neurons in cortex. We propose that such tuning is a consequence of normalization or gain control circuits. We also present a biologically plausible neural circuitry ...
Neural Voting Machines
(2004-12-31)
 Winner-take-all networks typically pick as winners that alternative with the largest excitatory input. This choice is far from optimal when there is uncertainty in the strength of the inputs, and when information is ...