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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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Machine Learning Systems for Intelligent Services in the IoT: A Survey.
Wiebke Toussaint,Aaron Yi Ding +1 more
TL;DR: This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT.
Proceedings ArticleDOI
Learning Where To Look - Generative NAS is Surprisingly Efficient
TL;DR: A generative model, paired with a surrogate predictor, that iteratively learns to generate samples from increasingly promising latent subspaces leads to very effective and efficient architecture search, while keeping the query amount low.
Proceedings ArticleDOI
Learning to Learn by Jointly Optimizing Neural Architecture and Weights
TL;DR: In this article , the authors proposed a Connection-Adaptive Meta-Learning (CAML) method to obtain better meta-learners by co-optimizing the architecture and meta-weights simultaneously.
Journal ArticleDOI
Neural Architecture Search for Dense Prediction Tasks in Computer Vision
Thomas Elsken,Arber Zela,Jan Hendrik Metzen,Benedikt Staffler,Thomas Brox,Abhinav Valada,Frank Hutter +6 more
TL;DR: An overview of neural architecture search for dense prediction tasks can be found in this paper , where the authors provide an overview of the challenges and ways to address them to ease future research and apply existing methods to novel problems.
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Exploiting the Redundancy in Convolutional Filters for Parameter Reduction
TL;DR: This work introduces a correlation-based regularization loss to achieve such flexibility over redundancy, and control the number of parameters in turn, and designs a plug-and-play layer to conveniently replace a conventional convolutional layer, without any additional changes required in the network architecture or the hyper-parameter settings.
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.