Open AccessPosted Content
Neural Architecture Search with Reinforcement Learning
Barret Zoph,Quoc V. Le +1 more
TLDR
This paper uses a recurrent network to generate the model descriptions of neural networks and trains this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set.Abstract:
Neural networks are powerful and flexible models that work well for many difficult learning tasks in image, speech and natural language understanding. Despite their success, neural networks are still hard to design. In this paper, we use a recurrent network to generate the model descriptions of neural networks and train this RNN with reinforcement learning to maximize the expected accuracy of the generated architectures on a validation set. On the CIFAR-10 dataset, our method, starting from scratch, can design a novel network architecture that rivals the best human-invented architecture in terms of test set accuracy. Our CIFAR-10 model achieves a test error rate of 3.65, which is 0.09 percent better and 1.05x faster than the previous state-of-the-art model that used a similar architectural scheme. On the Penn Treebank dataset, our model can compose a novel recurrent cell that outperforms the widely-used LSTM cell, and other state-of-the-art baselines. Our cell achieves a test set perplexity of 62.4 on the Penn Treebank, which is 3.6 perplexity better than the previous state-of-the-art model. The cell can also be transferred to the character language modeling task on PTB and achieves a state-of-the-art perplexity of 1.214.read more
Citations
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Journal ArticleDOI
Review: Deep Learning in Electron Microscopy
TL;DR: In this paper, a review of deep learning in electron microscopy is presented, with a focus on hardware and software needed to get started with deep learning and interface with electron microscopes.
Posted Content
Multi-objective Architecture Search for CNNs.
TL;DR: This work proposes NASH, an architecture search which considerable reduces the computational resources required for training novel architectures by applying network morphisms and aggressive learning rate schedules and proposes Pareto-NASH, a method for multi-objective architecture search that allows approximating the Pare to-front of architectures under multiple objective, such as predictive performance and number of parameters, in a single run of the method.
Journal ArticleDOI
Applications of machine learning techniques in side-channel attacks: a survey
TL;DR: A target-based classification is used to differentiate published work and drive general conclusions according to a common machine learning workflow that enables researchers to gain a better understanding of the topic and to design new attack methods as well as potential countermeasures.
Journal ArticleDOI
CNN-Based Super-Resolution of Hyperspectral Images
TL;DR: The design of 3-D convolutional neural network (CNN)-based SISR architectures that can map the spatial–spectral characteristics of hypercubes to a finer spatial resolution are proposed and better accuracy of the proposed frameworks compared to the prominent approaches is confirmed.
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Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity
TL;DR: This paper investigates a modular co-evolution strategy: a collection of primitive agents learns to dynamically self-assemble into composite bodies while also learning to coordinate their behavior to control these bodies.
References
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Proceedings Article
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Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
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Proceedings ArticleDOI
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Navneet Dalal,Bill Triggs +1 more
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