70,285 research outputs found
The use of adversaries for optimal neural network training
B-decay data from the Belle experiment at the KEKB collider have a
substantial background from events. To suppress this
we employ deep neural network algorithms. These provide improved signal from
background discrimination. However, the deep neural network develops a
substantial correlation with the kinematic variable used to
distinguish signal from background in the final fit due to its relationship
with input variables. The effect of this correlation is reduced by deploying an
adversarial neural network. Overall the adversarial deep neural network
performs better than a Boosted Decision Tree algorithimn and a commercial
package, NeuroBayes, which employs a neural net with a single hidden layer
Lazy Evaluation of Convolutional Filters
In this paper we propose a technique which avoids the evaluation of certain
convolutional filters in a deep neural network. This allows to trade-off the
accuracy of a deep neural network with the computational and memory
requirements. This is especially important on a constrained device unable to
hold all the weights of the network in memory
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