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Neural Architecture Search with Reinforcement Learning
Barret Zoph,Quoc V. Le +1 more
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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
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Improving One-Shot NAS with Shrinking-and-Expanding Supernet
TL;DR: Zhang et al. as discussed by the authors proposed a shrinking-and-expanding supernet that decouples the shared parameters by reducing the degree of weight sharing, avoiding unstable and inaccurate performance estimation.
Journal ArticleDOI
Federated mmWave Beam Selection Utilizing LIDAR Data
TL;DR: In this paper, a federated LIDAR aided beam selection method for V2I mmWave communication systems is proposed, where connected vehicles collaborate to train a shared neural network (NN) on their locally available LidAR data during normal operation.
Proceedings ArticleDOI
Towards a Unified Framework for Visual Compatibility Prediction
TL;DR: A unified framework for compatibility learning that is jointly conditioned on the type, context, and style is introduced and significantly outperform existing state-of-the- art methods.
Proceedings ArticleDOI
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
TL;DR: One-shot neural ensemble architecture search (NEAS) as mentioned in this paper proposes to search for multiple diverse models simultaneously as an alternative way to find powerful models by learning layer sharing among different models for efficiency purposes.
Journal ArticleDOI
Hyperspectral-cube-based mobile face recognition: A comprehensive review
Xianyi Zhang,Haitao Zhao +1 more
TL;DR: Results show that under harsh conditions like large illumination changing and pose variation, hyperspectral-cube- based methods have higher recognition accuracy than visible-image-based methods.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Proceedings ArticleDOI
Histograms of oriented gradients for human detection
Navneet Dalal,Bill Triggs +1 more
TL;DR: It is shown experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection, and the influence of each stage of the computation on performance is studied.