Fetching the paper…
Reading the bibliography…
While recent NAS algorithms are thousands of times faster than the pioneering works, it is often overlooked that they use fewer candidate operations, resulting in a significantly smaller search space.
1906
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images”,
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever and G. E. Hinton, “ImageNet classification with deep convolutional neural networks”, In
2012
Earlier work this paper cites.
T. Chen, I. Goodfellow and J. Shlens, “Net2Net: Accelerating Learning via Knowledge Transfer”, Arxiv, 1511.05641, 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization”, In
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, J. Sun, “Identity Mappings in Deep Residual Networks”, Arxiv, 1603.050274, 2016
2016
Earlier work this paper cites.
G. Huang, Z. Liu, L. van der Maaten and K. Q. Weinberger, “Densely Connected Convolutional Networks”, Arxiv, 1608.06993, 2016
2016
Earlier work this paper cites.
D. Han, J. Kim and J. Kim, “Deep Pyramidal Residual Networks”, Arxiv, 1610.02915, 2016
2016
Earlier work this paper cites.
B. Zoph and Q. V. Le, “Neural Architecture Search with Reinforcement Learning”, Arxiv, 1611.01578, 2016
2016
Earlier work this paper cites.
T. Wei, C. Wang, Y. Rui and C. W. Chen, “Network Morphism”, Arxiv, 1603.01670, 2016
2016
Earlier work this paper cites.
G. Larsson, M. Maire and G. Shakhnarovich, “FRACTALNET: ULTRA-DEEP NEURAL NETWORKS WITHOUT RESIDUALS”, Arxiv, 1605.07648v4, 2016
2016
Cited alongside, same era.
C. J. Maddison, A. Mnih and Y. Whye Teh, “The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables”, Arxiv, 1611.00712, 2016
2016
Cited alongside, same era.
B. Zoph, V. Vasudevan, J. Shlens and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition”, Arxiv, 1707.07012, 2017
2017
Cited alongside, same era.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L. Li, L. Fei-Fei, A. Yuille, J. Huang and K. Murphy, “Progressive Neural Architecture Search”, Arxiv, 1712.00559, 2017
2017
Cited alongside, same era.
T. Elsken, J. Metzen and F. Hutter, “Simple And Efficient Architecture Search for Convolutional Neural Networks”, Arxiv, 1711.04528, 2017
2017
H. Cai, J. Yang, W. Zhang, S. Han and Y. Yu, “Path-Level Network Transformation for Efficient Architecture Search”, Arxiv, 1806.02639, 2018
2018
Later among the works it cites.
S. Xie, H. Zheng, C. Liu and L. Lin, “SNAS: Stochastic Neural Architecture Search”, Arxiv, 1812.09926, 2018
2018
Later among the works it cites.
X. Chen, L. Xie, J. Wu and Q. Tian, “Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation”, Arxiv, 1904.12760, 2019
2019
Closest in time.
A. Hundt, V. Jain and G. Hager, “sharpDARTS: Faster and More Accurate Differentiable Architecture Search”, Arxiv, 1903.09900, 2019
2019
Closest in time.
A. Noy, N. Nayman, T. Ridnik, N. Zamir, S. Doveh, I. Friedman, R. Giryes and L. Zelnik-Manor, “ASAP: Architecture Search, Anneal and Prune”, Arxiv, 1904.04123, 2019
2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
T. DeVries and G. W. Taylor, “Improved Regularization of Convolutional Neural Networks with Cutout”, Arxiv, 1708.04552, 2017
2017
Cited alongside, same era.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L-C. Chen, “MobileNetV2: Inverted Residuals and Linear Bottlenecks”, Arxiv, 1801.04381, 2018
2018
Cited alongside, same era.
E. Real, A. Aggarwal, Y. Huang and Q. V. Le, “Regularized Evolution for Image Classifier Architecture Search”, Arxiv, 1802.01548, 2018
2018
Cited alongside, same era.
H. Pham, M. Y. Guan, B. Zoph, Q. V. Le and J. Dean, “Efficient Neural Architecture Search via Parameter Sharing”, Arxiv, 1802.03268, 2018
2018
Cited alongside, same era.
H. Liu, K. Simonyan and Y. Yang, “DARTS: Differentiable Architecture Search”, Arxiv, 1806.09055, 2018
2018
Cited alongside, same era.
Closest in time.
X. Zheng, R. Ji, L. Tang, B. Zhang, J. Liu and Q. Tian, “Multinomial Distribution Learning for Effective Neural Architecture Search”, Arxiv, 1905.07529v1, 2019
2019
Closest in time.
C. Ying, A. Klein, E. Real, E. Christiansen, K. Murphy and F. Hutter, “NAS-Bench-101: Towards Reproducible Neural Architecture Search”, Arxiv, 1902.09635, 2019
2019
Closest in time.
I. Radosavovic, J. Johnson, S. Xie, W-Y. Lo and P. Dollár, “On Network Design Spaces for Visual Recognition”, Arxiv, 1905.13214, 2019
2019
Closest in time.
Q. Yao, J. Xu, W-W. Tu and Z. Zhu, “Differentiable Neural Architecture Search via Proximal Iterations”, Arxiv, 1905.13577, 2019
2019
Closest in time.