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
Citations
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Proceedings ArticleDOI
Worst Perception Scenario Search for Autonomous Driving
TL;DR: In this paper, a single layer recurrent neural network with LSTM neurons is employed to predict WPS according to the searching reward, which is optimized by a vanilla policy gradient method.
Posted Content
NetTailor: Tuning the Architecture, Not Just the Weights
Pedro Morgado,Nuno Vasconcelos +1 more
TL;DR: NetTailor as discussed by the authors uses a soft-attention mechanism over blocks and complexity regularization constraints to adapt the network architecture, not just its weights, to the target task.
Posted Content
Differentiable Neural Architecture Search via Proximal Iterations.
TL;DR: Different from DARTS, NASP reformulates the search process as an optimization problem with a constraint that only one operation is allowed to be updated during forward and backward propagation, and proposes a new algorithm inspired by proximal iterations to solve it.
Proceedings ArticleDOI
NeuralScale: Efficient Scaling of Neurons for Resource-Constrained Deep Neural Networks
Eugene Lee,Chen-Yi Lee +1 more
TL;DR: This work attempts to search for the neuron (filter) configuration of a fixed network architecture that maximizes accuracy using iterative pruning methods as a proxy, and introduces architecture descent which iteratively refines the parametrized function used for model scaling.
Proceedings Article
BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
TL;DR: BRECQ as mentioned in this paper proposes a novel post-training quantization framework, dubbed BRECQ, which pushes the limits of bitwidth in PTQ down to INT2 for the first time.
References
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Proceedings ArticleDOI
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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.