Neural architecture search with reinforcement

• [PDF File]Progressive Neural Architecture Search

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  Progressive Neural Architecture Search Chenxi Liu1⋆, Barret Zoph2, Maxim Neumann2, Jonathon Shlens2, Wei Hua2, Li-Jia Li2, Li Fei-Fei2,3, Alan Yuille1, Jonathan Huang2, and Kevin Murphy2 1 Johns Hopkins University 2 Google AI 3 Stanford University Abstract. We propose a new method for learning the structure of con-volutional neural networks (CNNs) that is more efficient than recent

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• [PDF File]Automatic Search of Neural Network Architectures

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  Google introduced the idea of implementing Neural Network Search by employing evolutionary algorithms and reinforcement learning in order to design and find optimal neural network architecture. In essence, what this is doing is that it is training to create a layer and then stacking those layers to create a Deep Neural Network architecture.

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• [PDF File]GraphNAS: Graph Neural Architecture Search with ...

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  GraphNAS: Graph Neural Architecture Search with Reinforcement Learning Yang Gao 1, Hong Yang 2, Peng Zhang 3, Chuan Zhou 4, Yue Hu 5 fgaoyang, zhouchuan,huyueg@iie.ac.cn, hong.yang@student.uts.edu ...

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• [PDF File]ImprovingNeuralArchitecture SearchwithReinforcementLearning

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  To have a better overall picture of Neural Architecture Search, we offer a concise introduction into the topic of Deep Learning, followed by an overview of various typesofneuralnetworklayers.

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• [PDF File]Neural Architecture Search

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  Search via Reinforcement Learning NAS-RL 31 • Performance is on-par with other CNNs of the time Under review as a conference paper at ICLR 2017 AAPPENDIX Figure 7: Convolutional architecture discovered by our method, when the search space does not

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• [PDF File]Graph Neural Architecture Search - IJCAI

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  of using reinforcement learning to design the best graph neural architecture. We present a new model GraphNAS to enable the auto-matic search of the best graph neural architecture, where a new search space is designed that covers the operators from the state-of-the-art GNNs, and a policy gradient al-gorithm is used to iteratively solve the problem.

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• [PDF File]Neural Architecture Search for Dense Prediction Tasks

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  Instead, neural architecture search (NAS) methods automatically find “right” configurations for any given task Problem: Current reinforcement learning (RL)-based approaches for dense-per-pixel tasks (segmentation / depth / etc.) require hundreds or even thousands of GPU-days

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• [PDF File]Neural Architecture Meta-learning via Reinforcement

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  Neural Architecture Meta-learning via Reinforcement by Sagnik Majumder Neural architecture search is a very rapidly growing domain with the objectives being the mitigation of the problems that come along with hand-engineering neural network architectures and the production of very good performance in di erent machine learning applications. Use of

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• [PDF File]Reinforcement Learning based Neural Architecture Search ...

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  Reinforcement Learning based Neural Architecture Search for Audio Tagging Haiyang Liu Graduate School of Information, Production and Systems Waseda University Tokyo, Japan haiyangliu@toki.waseda.jp Cheng Zhang School of Instrument Science and Engineering Southeast University Nanjing, China 213172942@seu.edu.cn

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• [PDF File]Neural Architechture Search with Reinforcement Learning

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  Neural Architecture Search with Reinforcement Learning • Neural networks are powerful and widely used. • A good model structure of NN will be beneficial. • Many modern neural networks perform well/better only with specific structures, e.g., LSTM in RNN and skip connections in CNN

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• NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING

  Neural Architecture Search can find a novel ConvNet model that is better than most human-invented architectures. Our CIFAR-10 model achieves a 3.65 test set error, while being 1.05x faster than the current best model. On language modeling with Penn Treebank, Neural Architecture Search can

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• [PDF File]Neural Architecture Search With Reinforcement Learning

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  Neural Architecture Search, an idea of using a recurrent neural network to compose neural network architectures. By using recurrent network as the controller, our method is flexible so that it can search variable-length architecture space. This method has strong empirical performance on very challenging benchmarks and presents a new research ...

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• [PDF File]Neural Architecture Search with Bayesian Optimisation and ...

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  Neural Architecture Search { Prior Work Based on Reinforcement Learning: (Baker et al. 2016, Zhong et al. 2017, Zoph & Le 2017, Zoph et al. 2017) RL is more di cult than optimisation (Jiang et al. 2016). Based on Evolutionary Algorithms: (Kitano 1990, Stanley & Miikkulainen 2002, Floreano et al. 2008, Liu et al. 2017,

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• [PDF File]Neural Architecture Search and Beyond

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  Confidential + Proprietary How does architecture search work? Sample models from search space Trainer Reward Controller Accuracy Reinforcement Learning

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• [PDF File]Designing Neural Network Architectures Using Reinforcement ...

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  The key innovation is to reformulate the network architecture search as a reinforcement learning task! - State space: all possible neural net architectures - Action space: choosing new layers (conv, FC, pool) to put in the network - Reward function: the validation accuracy of the complete model

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• [PDF File]Neural Architecture Search A Tutorial - Meta-Learning for ...

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  • Neural architecture search can generate state-of-the-art architectures for computer vision tasks given a well-defined search space • Develop new optimizers, activation functions, data augmentation strategies • Architecture search can be especially useful for designing efficient architectures for resource-constrained settings

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• [PDF File]Neural Architecture Search with Reinforcement Learning

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  Neural Architecture Search with Reinforcement Learning Barret Zoph & Quoc Le. Motivation for Architecture Search Designing neural network architectures is hard Lots of human efforts go into tuning them There is not a lot of intuition into how to design them well

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• EFFICIENT GRAPH NEURAL ARCHITECTURE SEARCH

  Recently, graph neural networks (GNN) have been demonstrated effective in vari-ous graph-based tasks. To obtain state-of-the-art (SOTA) data-specific GNN archi-tectures, researchers turn to the neural architecture search (NAS) methods. How-ever, it remains to be a challenging problem to conduct efficient architecture search for GNN.

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• [PDF File]Jon Shlens, Google Brain team Vijay Vasudevan, Irwan Bello ...

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  Neural Architecture Search with Reinforcement Learning Quoc Le & Barret Zoph Thanks: Vijay Vasudevan, Irwan Bello, Jon Shlens, Google Brain team. ... Use reinforcement learning to update the parameters of the Controller model based on the accuracy of the child model. Neural Architecture Search .

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