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
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FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
Alvin Wan,Xiaoliang Dai,Peizhao Zhang,Zijian He,Yuandong Tian,Saining Xie,Bichen Wu,Matthew Yu,Tao Xu,Kan Chen,Peter Vajda,Joseph E. Gonzalez +11 more
TL;DR: This work proposes a memory and computationally efficient DNAS variant, DMaskingNAS, that expands the search space by up to 10^14x over conventional DNAS, supporting searches over spatial and channel dimensions that are otherwise prohibitively expensive: input resolution and number of filters.
Proceedings ArticleDOI
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
Cong Hao,Xiaofan Zhang,Yuhong Li,Sitao Huang,Jinjun Xiong,Kyle Rupnow,Wen-mei W. Hwu,Deming Chen +7 more
TL;DR: Results show that the proposed DNN model and accelerator outperform the state-of-the-art FPGA designs in all aspects including Intersection-over-Union (IoU) and energy efficiency.
Journal ArticleDOI
Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics
TL;DR: This study adopts CNNs to link experimental microstructures with corresponding ionic conductivities to train convolutional neural networks, and reveals that CNNs can be trained using only seven micrographs, and their performance exceeds the conventional scheme using hand-crafted features.
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Reinforced Continual Learning
Ju Xu,Zhanxing Zhu +1 more
TL;DR: The authors proposed Reinforced Continual Learning (RL) which searches for the best neural architecture for each coming task via sophisticatedly designed reinforcement learning strategies, which not only has good performance on preventing catastrophic forgetting but also fits new tasks well.
Journal ArticleDOI
Evolutionary convolutional neural networks: An application to handwriting recognition
TL;DR: This work has explored the application of neuroevolution to the automatic design of CNN topologies, introducing a common framework for this task and developing two novel solutions based on genetic algorithms and grammatical evolution.
References
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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.
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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
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.