81,722 research outputs found
Deep Epitomic Convolutional Neural Networks
Deep convolutional neural networks have recently proven extremely competitive
in challenging image recognition tasks. This paper proposes the epitomic
convolution as a new building block for deep neural networks. An epitomic
convolution layer replaces a pair of consecutive convolution and max-pooling
layers found in standard deep convolutional neural networks. The main version
of the proposed model uses mini-epitomes in place of filters and computes
responses invariant to small translations by epitomic search instead of
max-pooling over image positions. The topographic version of the proposed model
uses large epitomes to learn filter maps organized in translational
topographies. We show that error back-propagation can successfully learn
multiple epitomic layers in a supervised fashion. The effectiveness of the
proposed method is assessed in image classification tasks on standard
benchmarks. Our experiments on Imagenet indicate improved recognition
performance compared to standard convolutional neural networks of similar
architecture. Our models pre-trained on Imagenet perform excellently on
Caltech-101. We also obtain competitive image classification results on the
small-image MNIST and CIFAR-10 datasets.Comment: 9 page
Finding strong lenses in CFHTLS using convolutional neural networks
We train and apply convolutional neural networks, a machine learning
technique developed to learn from and classify image data, to
Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the
identification of potential strong lensing systems. An ensemble of four
convolutional neural networks was trained on images of simulated galaxy-galaxy
lenses. The training sets consisted of a total of 62,406 simulated lenses and
64,673 non-lens negative examples generated with two different methodologies.
The networks were able to learn the features of simulated lenses with accuracy
of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000
simulations. An ensemble of trained networks was applied to all of the 171
square degrees of the CFHTLS wide field image data, identifying 18,861
candidates including 63 known and 139 other potential lens candidates. A second
search of 1.4 million early type galaxies selected from the survey catalog as
potential deflectors, identified 2,465 candidates including 117 previously
known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266
novel probable or potential lenses and 2097 candidates we classify as false
positives. For the catalog-based search we estimate a completeness of 21-28%
with respect to detectable lenses and a purity of 15%, with a false-positive
rate of 1 in 671 images tested. We predict a human astronomer reviewing
candidates produced by the system would identify ~20 probable lenses and 100
possible lenses per hour in a sample selected by the robot. Convolutional
neural networks are therefore a promising tool for use in the search for lenses
in current and forthcoming surveys such as the Dark Energy Survey and the Large
Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
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