1,923 research outputs found
EDEN: Evolutionary Deep Networks for Efficient Machine Learning
Deep neural networks continue to show improved performance with increasing
depth, an encouraging trend that implies an explosion in the possible
permutations of network architectures and hyperparameters for which there is
little intuitive guidance. To address this increasing complexity, we propose
Evolutionary DEep Networks (EDEN), a computationally efficient
neuro-evolutionary algorithm which interfaces to any deep neural network
platform, such as TensorFlow. We show that EDEN evolves simple yet successful
architectures built from embedding, 1D and 2D convolutional, max pooling and
fully connected layers along with their hyperparameters. Evaluation of EDEN
across seven image and sentiment classification datasets shows that it reliably
finds good networks -- and in three cases achieves state-of-the-art results --
even on a single GPU, in just 6-24 hours. Our study provides a first attempt at
applying neuro-evolution to the creation of 1D convolutional networks for
sentiment analysis including the optimisation of the embedding layer.Comment: 7 pages, 3 figures, 3 tables and see video
https://vimeo.com/23451009
Driver Distraction Identification with an Ensemble of Convolutional Neural Networks
The World Health Organization (WHO) reported 1.25 million deaths yearly due
to road traffic accidents worldwide and the number has been continuously
increasing over the last few years. Nearly fifth of these accidents are caused
by distracted drivers. Existing work of distracted driver detection is
concerned with a small set of distractions (mostly, cell phone usage).
Unreliable ad-hoc methods are often used.In this paper, we present the first
publicly available dataset for driver distraction identification with more
distraction postures than existing alternatives. In addition, we propose a
reliable deep learning-based solution that achieves a 90% accuracy. The system
consists of a genetically-weighted ensemble of convolutional neural networks,
we show that a weighted ensemble of classifiers using a genetic algorithm
yields in a better classification confidence. We also study the effect of
different visual elements in distraction detection by means of face and hand
localizations, and skin segmentation. Finally, we present a thinned version of
our ensemble that could achieve 84.64% classification accuracy and operate in a
real-time environment.Comment: arXiv admin note: substantial text overlap with arXiv:1706.0949
Optimization of Convolutional Neural Network ensemble classifiers by Genetic Algorithms
Breast cancer exhibits a high mortality rate and it is the most invasive cancer in women. An analysis from histopathological images could predict this disease. In this way, computational image processing might support this task. In this work a proposal which employes deep learning convolutional neural networks is presented. Then, an ensemble of networks is considered in order to obtain an enhanced recognition performance of the system by the consensus of the networks of the ensemble. Finally, a genetic algorithm is also considered to choose the networks that belong to the ensemble. The proposal has been tested by carrying out several experiments with a set of benchmark images.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tech
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