194,287 research outputs found
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
Acoustic Performance of Exhaust Muffler Based Genetic Algorithms and Artificial Neural Network
The noise level was one of the important indicators as a measure of the quality and performance of the diesel engine.Exhaust noise in diesel engines machine accounted for an important proportion of installed performance exhaust muffler and it was an effective way to control exhaust noise. This article using orthogonal test program for the muffler structure parameters as input to the sound pressure level and diesel fuel each output artificial neural network (BP network) learning sample. Matlab artificial neural network toolbox to complete the training of the network, and better noise performance and fuel consumption rate performance muffler internal structure parameters combination was obtained through genetic algorithm gifted collaborative validation of artificial neural networks and genetic algorithms to optimize application exhaust muffler design is entirely feasible
Neural network computing using on-chip accelerators
The use of neural networks, machine learning, or artificial intelligence, in its broadest and most controversial sense, has been a tumultuous journey involving three distinct hype cycles and a history dating back to the 1960s. Resurgent, enthusiastic interest in machine learning and its applications bolsters the case for machine learning as a fundamental computational kernel. Furthermore, researchers have demonstrated that machine learning can be utilized as an auxiliary component of applications to enhance or enable new types of computation such as approximate computing or automatic parallelization. In our view, machine learning becomes not the underlying application, but a ubiquitous component of applications. This view necessitates a different approach towards the deployment of machine learning computation that spans not only hardware design of accelerator architectures, but also user and supervisor software to enable the safe, simultaneous use of machine learning accelerator resources.
In this dissertation, we propose a multi-transaction model of neural network computation to meet the needs of future machine learning applications. We demonstrate that this model, encompassing a decoupled backend accelerator for inference and learning from hardware and software for managing neural network transactions can be achieved with low overhead and integrated with a modern RISC-V microprocessor. Our extensions span user and supervisor software and data structures and, coupled with our hardware, enable multiple transactions from different address spaces to execute simultaneously, yet safely. Together, our system demonstrates the utility of a multi-transaction model to increase energy efficiency improvements and improve overall accelerator throughput for machine learning applications
How Researchers Use Diagrams in Communicating Neural Network Systems
Neural networks are a prevalent and effective machine learning component, and
their application is leading to significant scientific progress in many
domains. As the field of neural network systems is fast growing, it is
important to understand how advances are communicated. Diagrams are key to
this, appearing in almost all papers describing novel systems. This paper
reports on a study into the use of neural network system diagrams, through
interviews, card sorting, and qualitative feedback structured around
ecologically-derived examples. We find high diversity of usage, perception and
preference in both creation and interpretation of diagrams, examining this in
the context of existing design, information visualisation, and user experience
guidelines. Considering the interview data alongside existing guidance, we
propose guidelines aiming to improve the way in which neural network system
diagrams are constructed.Comment: 19 pages, 6 tables, 3 figure
Automatically Designing CNN Architectures for Medical Image Segmentation
Deep neural network architectures have traditionally been designed and
explored with human expertise in a long-lasting trial-and-error process. This
process requires huge amount of time, expertise, and resources. To address this
tedious problem, we propose a novel algorithm to optimally find hyperparameters
of a deep network architecture automatically. We specifically focus on
designing neural architectures for medical image segmentation task. Our
proposed method is based on a policy gradient reinforcement learning for which
the reward function is assigned a segmentation evaluation utility (i.e., dice
index). We show the efficacy of the proposed method with its low computational
cost in comparison with the state-of-the-art medical image segmentation
networks. We also present a new architecture design, a densely connected
encoder-decoder CNN, as a strong baseline architecture to apply the proposed
hyperparameter search algorithm. We apply the proposed algorithm to each layer
of the baseline architectures. As an application, we train the proposed system
on cine cardiac MR images from Automated Cardiac Diagnosis Challenge (ACDC)
MICCAI 2017. Starting from a baseline segmentation architecture, the resulting
network architecture obtains the state-of-the-art results in accuracy without
performing any trial-and-error based architecture design approaches or close
supervision of the hyperparameters changes.Comment: Accepted to Machine Learning in Medical Imaging (MLMI 2018
Towards out-of-distribution generalization in large-scale astronomical surveys: robust networks learn similar representations
The generalization of machine learning (ML) models to out-of-distribution
(OOD) examples remains a key challenge in extracting information from upcoming
astronomical surveys. Interpretability approaches are a natural way to gain
insights into the OOD generalization problem. We use Centered Kernel Alignment
(CKA), a similarity measure metric of neural network representations, to
examine the relationship between representation similarity and performance of
pre-trained Convolutional Neural Networks (CNNs) on the CAMELS Multifield
Dataset. We find that when models are robust to a distribution shift, they
produce substantially different representations across their layers on OOD
data. However, when they fail to generalize, these representations change less
from layer to layer on OOD data. We discuss the potential application of
similarity representation in guiding model design, training strategy, and
mitigating the OOD problem by incorporating CKA as an inductive bias during
training.Comment: Accepted to Machine Learning and the Physical Sciences Workshop,
NeurIPS 202
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