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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Journal ArticleDOI
An Energy-Efficient Mixed-Bit CNN Accelerator With Column Parallel Readout for ReRAM-Based In-Memory Computing
Dingbang Liu,Haoxiang Zhou,Wei Mao,Jun Liu,Yuliang Han,Changhai Man,Qiuping Wu,Zhiru Guo,Mingqiang Huang,Shaobo Luo,Mingsong Lv,Quan Chen,Hao Yu +12 more
TL;DR: In this paper , a ReRAM-based CNN accelerator is designed to implement Neural Architecture Search (NAS)-optimized layer-wise multi-bit CNNs, and column-parallel readout is achieved with excellent energy-efficient performance by a variation reduction accumulation mechanism and low power readout circuits.
Posted Content
MAPLE: Microprocessor A Priori for Latency Estimation
TL;DR: In this article, the authors proposed Microprocessor A Priori for Latency Estimation (MAPLE) which takes advantage of a novel quantitative strategy to characterize the underlying microprocessor by measuring relevant hardware performance metrics, yielding a finegrained and expressive hardware descriptor.
Proceedings ArticleDOI
Fast-PPO: Proximal Policy Optimization with Optimal Baseline Method
TL;DR: In this article, the authors proposed a new method of PPO with the optimal baseline called Fast-PPO, which considers both the advantage of action estimate and the estimate of accumulative reward.
Proceedings ArticleDOI
An Approach for Asbestos-related Pleural Plaque Detection
Azael M. Sousa,César Castelo-Fernández,Daniel Osaku,Ericson Bagatin,Fabiano Reis,Alexandre X. Falcão +5 more
TL;DR: A pipeline for asbestos-related pleural plaque detection in CT images of the human thorax based on the following operations: lung segmentation, 3D patch selection along the pleura, a convolutional neural network (CNN) for feature extraction, and classification by support vector machines (SVM).
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On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective.
TL;DR: It is demonstrated that knowledge distillation methods are orthogonal to other efficiency-enhancing methods both analytically and empirically, and ways to integrate it with other methods effectively are introduced.
References
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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
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.