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Neural Architecture Search with Reinforcement Learning
Barret Zoph,Quoc V. Le +1 more
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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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Journal ArticleDOI
Evaluation of Diverse Convolutional Neural Networks and Training Strategies for Wheat Leaf Disease Identification with Field-Acquired Photographs
TL;DR: In this article , the authors presented an assessment of seven typical CNNs (VGG-16, Inception-v3, ResNet-50, DenseNet-121, EfficentNet-B6, ShuffleNet-v2 and MobileNetV3) for the identification of wheat main leaf diseases (powdery mildew, leaf rust and stripe rust) using field images.
Journal ArticleDOI
Contrastive Self-supervised Neural Architecture Search.
Nam Nguyen,J. Morris Chang +1 more
TL;DR: Li et al. as discussed by the authors proposed a cell-based neural architecture search algorithm (NAS), which completely alleviates the expensive costs of data labeling inherited from supervised learning by using only a small amount of unlabeled train data under contrastive self-supervised learning.
Proceedings ArticleDOI
Towards Automatic Discovering of Deep Hybrid Network Architecture for Sequential Recommendation
TL;DR: This work designs a Table-like search space, involving both self-attentive and convolutional-based SR architectures in a flexible manner, and proposes NASR, an efficient neural architecture search (NAS) method that can automatically select the architecture operation on each layer.
Posted Content
Finding Non-Uniform Quantization Schemes using Multi-Task Gaussian Processes
TL;DR: A novel method for neural network quantization that casts the neural architecture search problem as one of hyperparameter search to find non-uniform bit distributions throughout the layers of a CNN to achieve a minimal loss of accuracy with appreciable memory savings is proposed.
Journal ArticleDOI
Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction
TL;DR: An automated dilated spatio-temporal synchronous graph network, named Auto-DSTSGN for traffic prediction, that can achieve about 10% improve- ments compared with the state-of-art methods.
References
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Proceedings Article
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Diederik P. Kingma,Jimmy Ba +1 more
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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.