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Neural Architecture Search (NAS) is a laborious process.
Large-Scale Evolution of Image Classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. Le, and A. Kurakin · 1938
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
W. R.J · 1992
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2014
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Deep Speech 2: End-to-End Speech Recognition in English and Mandarin
D. Amodei, R. Anubhai, E. Battenberg, C. Case, J. Casper, B. Catanzaro, J. Chen, M. Chrzanowski, A. Coates, G. Diamos, E. Elsen, J. Engel, L. Fan, C. Fougner, T. Han, A. Hannun, B. Jun, P. LeGresley, L. Lin, S. Narang, A. Ng, S. Ozair, R. Prenger, J. Raiman, S. Satheesh, D. Seetapun, S. Sengupta, Y. Wang, Z. Wang, C. Wang, B. Xiao, D. Yogatama, J. Zhan, and Z. Zhu · 2015
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Learning both weights and connections for efficient neural networks
S. Han, J. Pool, J. Tran, and W. J. Dally · 2015
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Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Sparse convolutional neural networks
B. Liu, M. Wang, H. Foroosh, M. Tappen, and M. Penksy · 2015
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Structured Transforms for Small-Footprint Deep Learning
V. Sindhwani, T. N. Sainath, and S. Kumar · 2015
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
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Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhutdinov, R. Zemel, and Y. Bengio · 2015
Cited alongside, same era.
Neural Architecture Search with Reinforcement Learning
B. Zoph and Q. V. Le · 2015
Cited alongside, same era.
Designing Neural Network Architectures using Reinforcement Learning
B. Baker, O. Gupta, N. Naik, and R. Raskar · 2016
Cited alongside, same era.
Xception: Deep Learning with Depthwise Separable Convolutions
F. Chollet · 2016
Cited alongside, same era.
Quantized neural networks: Training neural networks with low precision weights and activations
I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio · 2016
Cited alongside, same era.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
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DeepArchitect: Automatically Designing and Training Deep Architectures
R. Negrinho and G. Gordon · 2017
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Parallel WaveNet: Fast High-Fidelity Speech Synthesis
A. van den Oord, Y. Li, I. Babuschkin, K. Simonyan, O. Vinyals, K. Kavukcuoglu, G. van den Driessche, E. Lockhart, L. C. Cobo, F. Stimberg, N. Casagrande, D. Grewe, S. Noury, S. Dieleman, E. Elsen, N. Kalchbrenner, H. Zen, A. Graves, H. King, T. Walters, D. Belov, and D. Hassabis · 2017
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Hello Edge: Keyword Spotting on Microcontrollers
Y. Zhang, N. Suda, L. Lai, and V. Chandra · 2017
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Neural architecture search with bayesian optimisation and optimal transport
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SGDR: stochastic gradient descent with restarts
I. Loshchilov and F. Hutter · 2016
Cited alongside, same era.
N2N learning: Network to network compression via policy gradient reinforcement learning
A. Ashok, N. Rhinehart, F. Beainy, and K. M. Kitani · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P. Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, R. C. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon · 2017
Cited alongside, same era.
Accelerating Neural Architecture Search using Performance Prediction
B. Baker, O. Gupta, R. Raskar, and N. Naik
Cited in the paper.
Progressive Neural Architecture Search
C. Liu, B. Zoph, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy
Cited in the paper.
Hierarchical representations for efficient architecture search
H. Liu, K. Simonyan, O. Vinyals, C. Fernando, and K. Kavukcuoglu
Cited in the paper.
K. Kandasamy, W. Neiswanger, J. Schneider, B. Póczos, and E. Xing · 2018
Closest in time.
Efficient Neural Architecture Search via Parameter Sharing
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le, and J. Dean · 2018
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MobileNetV2: Inverted Residuals and Linear Bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
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Multi-objective architecture search for cnns
F. H. Thomas Elsken, Jan Hendrik Metzen · 2018
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Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
P. Warden · 2018
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