110,850 research outputs found
Practical recommendations for gradient-based training of deep architectures
Learning algorithms related to artificial neural networks and in particular
for Deep Learning may seem to involve many bells and whistles, called
hyper-parameters. This chapter is meant as a practical guide with
recommendations for some of the most commonly used hyper-parameters, in
particular in the context of learning algorithms based on back-propagated
gradient and gradient-based optimization. It also discusses how to deal with
the fact that more interesting results can be obtained when allowing one to
adjust many hyper-parameters. Overall, it describes elements of the practice
used to successfully and efficiently train and debug large-scale and often deep
multi-layer neural networks. It closes with open questions about the training
difficulties observed with deeper architectures
Neural networks optimization through genetic algorithm searches: A review
Neural networks and genetic algorithms are the two sophisticated machine learning techniques presently attracting attention
from scientists, engineers, and statisticians, among others. They have gained popularity in recent years. This paper presents a state of
the art review of the research conducted on the optimization of neural networks through genetic algorithm searches. Optimization is
aimed toward deviating from the limitations attributed to neural networks in order to solve complex and challenging problems. We
provide an analysis and synthesis of the research published in this area according to the application domain, neural network design
issues using genetic algorithms, types of neural networks and optimal values of genetic algorithm operators (population size, crossover
rate and mutation rate). This study may provide a proper guide for novice as well as expert researchers in the design of evolutionary
neural networks helping them choose suitable values of genetic algorithm operators for applications in a specific problem domain.
Further research direction, which has not received much attention from scholars, is unveiled
Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation
In this work, we develop a neural network-based method to convert a noisy
motion signal generated from segmenting rebinned list-mode cardiac SPECT
images, to that of a high-quality surrogate signal, such as those seen from
external motion tracking systems (EMTs). This synthetic surrogate will be used
as input to our pre-existing motion correction technique developed for EMT
surrogate signals. In our method, we test two families of neural networks to
translate noisy internal motion to external surrogate: 1) fully connected
networks and 2) convolutional neural networks. Our dataset consists of cardiac
perfusion SPECT acquisitions for which cardiac motion was estimated (input:
center-of-count-mass - COM signals) in conjunction with a respiratory surrogate
motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT
signals). We obtained an average R-score of 0.76 between the predicted
surrogate and the EMT signal. Our goal is to lay a foundation to guide the
optimization of neural networks for respiratory motion correction from SPECT
without the need for an EMT.Comment: Medical Imaging Meets NeurIP
Visual Steering for One-Shot Deep Neural Network Synthesis
Recent advancements in the area of deep learning have shown the effectiveness
of very large neural networks in several applications. However, as these deep
neural networks continue to grow in size, it becomes more and more difficult to
configure their many parameters to obtain good results. Presently, analysts
must experiment with many different configurations and parameter settings,
which is labor-intensive and time-consuming. On the other hand, the capacity of
fully automated techniques for neural network architecture search is limited
without the domain knowledge of human experts. To deal with the problem, we
formulate the task of neural network architecture optimization as a graph space
exploration, based on the one-shot architecture search technique. In this
approach, a super-graph of all candidate architectures is trained in one-shot
and the optimal neural network is identified as a sub-graph. In this paper, we
present a framework that allows analysts to effectively build the solution
sub-graph space and guide the network search by injecting their domain
knowledge. Starting with the network architecture space composed of basic
neural network components, analysts are empowered to effectively select the
most promising components via our one-shot search scheme. Applying this
technique in an iterative manner allows analysts to converge to the best
performing neural network architecture for a given application. During the
exploration, analysts can use their domain knowledge aided by cues provided
from a scatterplot visualization of the search space to edit different
components and guide the search for faster convergence. We designed our
interface in collaboration with several deep learning researchers and its final
effectiveness is evaluated with a user study and two case studies.Comment: 9 pages, submitted to IEEE Transactions on Visualization and Computer
Graphics, 202
An Expert System to Improve the Energy Efficiency of the Reaction Zone of a Petrochemical Plant
Energy is the most important cost factor in the petrochemical industry.
Thus, energy efficiency improvement is an important way to reduce these
costs and to increase predictable earnings, especially in times of high energy
price volatility. This work describes the development of an expert system for
the improvement of this efficiency of the reaction zone of a petrochemical
plant. This system has been developed after a data mining process of the variables
registered in the plant. Besides, a kernel of neural networks has been
embedded in the expert system. A graphical environment integrating the proposed
system was developed in order to test the system. With the application of
the expert system, the energy saving on the applied zone would have been about
20%.Junta de Andalucía TIC-570
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