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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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Learning Intrinsic Sparse Structures within Long Short-term Memory
Wei Wen,Yuxiong He,Samyam Rajbhandari,Minjia Zhang,Wenhan Wang,Fang Liu,Bin Hu,Yi Chen,Hai Li +8 more
TL;DR: In this article, the authors propose Intrinsic Sparse Structures (ISS) to learn structurally-sparse LSTM by reducing the sizes of basic structures within the network.
Book ChapterDOI
Cryptanalytic Extraction of Neural Network Models
TL;DR: It is argued that the machine learning problem of model extraction is actually a cryptanalytic problem in disguise, and should be studied as such, and given oracle access to a neural network, a differential attack is introduced that can efficiently steal the parameters of the remote model up to floating point precision.
Journal ArticleDOI
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
TL;DR: Auto-PyTorch as discussed by the authors combines multi-fidelity optimization with portfolio construction for warmstarting and ensembling of deep neural networks (DNNs) and common baselines for tabular data.
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Discrete Sequential Prediction of Continuous Actions for Deep RL
TL;DR: This paper shows how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions, and demonstrates empirically that the method can perform global search, which effectively gets around the local optimization issues that plague DDPG.
Proceedings ArticleDOI
ATMSeer: Increasing Transparency and Controllability in Automated Machine Learning
Qianwen Wang,Yao Ming,Zhihua Jin,Qiaomu Shen,Dongyu Liu,Micah J. Smith,Kalyan Veeramachaneni,Huamin Qu +7 more
TL;DR: The design and implementation of ATMSeer, an interactive visualization tool that supports users in refining the search space of AutoML and in analyzing the results, and a multi-granularity visualization is proposed to enable users to monitor the AutoML process, analyze the searched models, and refine thesearch space in real time.
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.