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Reinforcement Learning has yielded promising results for Neural Architecture Search (NAS).
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International conference on machine learning , 2016, pp. 1928–1937
1937
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” Machine learning , vol. 8, no. 3-4, pp. 229–256, 1992
1992
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
V. R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” in Advances in neural information processing systems , 2000, pp. 1008–1014
2000
Earlier work this paper cites.
C. K. Williams and C. E. Rasmussen, Gaussian processes for machine learning . MIT press Cambridge, MA, 2006, vol. 2, no. 3
2006
Earlier work this paper cites.
S. Bickel, M. Brückner, and T. Scheffer, “Discriminative learning under covariate shift.” Journal of Machine Learning Research , vol. 10, no. 9, 2009
2009
Earlier work this paper cites.
T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur, “Recurrent neural network based language model,” in Eleventh annual conference of the international speech communication association , 2010
2010
Earlier work this paper cites.
J. S. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl, “Algorithms for hyper-parameter optimization,” in Advances in neural information processing systems , 2011, pp. 2546–2554
2011
Earlier work this paper cites.
J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” Journal of machine learning research , vol. 13, no. Feb, pp. 281–305, 2012
2012
Earlier work this paper cites.
J. Snoek, H. Larochelle, and R. P. Adams, “Practical bayesian optimization of machine learning algorithms,” in Advances in neural information processing systems , 2012, pp. 2951–2959
2012
Earlier work this paper cites.
C. Thornton, F. Hutter, H. H. Hoos, and K. Leyton-Brown, “Auto-weka: Combined selection and hyperparameter optimization of classification algorithms,” in Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining , 2013, pp. 847–855
2013
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International conference on machine learning , 2013, pp. 1310–1318
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
D. Maclaurin, D. Duvenaud, and R. Adams, “Gradient-based hyperparameter optimization through reversible learning,” in International Conference on Machine Learning , 2015, pp. 2113–2122
2015
Earlier work this paper cites.
M. Germain, K. Gregor, I. Murray, and H. Larochelle, “Made: Masked autoencoder for distribution estimation,” in International Conference on Machine Learning , 2015, pp. 881–889
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in Proceedings of the International Conference on Learning Representations , 2015
2015
Earlier work this paper cites.
D. Harrison and D. Rubinfeld, “Boston housing dataset,” 2015
2015
Earlier work this paper cites.
J. Bergstra, B. Komer, C. Eliasmith, D. Yamins, and D. D. Cox, “Hyperopt: a python library for model selection and hyperparameter optimization,” Computational Science & Discovery , vol. 8, no. 1, p. 014008, 2015
2015
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Y. Kim, Y. Jernite, D. Sontag, and A. M. Rush, “Character-aware neural language models,” in Thirtieth AAAI Conference on Artificial Intelligence , 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2018
Later among the works it cites.
N. Fusi, R. Sheth, and M. Elibol, “Probabilistic matrix factorization for automated machine learning,” in Advances in neural information processing systems , 2018, pp. 3348–3357
2018
Later among the works it cites.
C. Liu, B. Zoph, M. Neumann, J. Shlens, W. Hua, L.-J. Li, L. Fei-Fei, A. Yuille, J. Huang, and K. Murphy, “Progressive neural architecture search,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 19–34
2018
Later among the works it cites.
2018
Later among the works it cites.
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2016
Cited alongside, same era.
B. Uria, M.-A. Côté, K. Gregor, I. Murray, and H. Larochelle, “Neural autoregressive distribution estimation,” The Journal of Machine Learning Research , vol. 17, no. 1, pp. 7184–7220, 2016
2016
Cited alongside, same era.
A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, and M. Figari, “Machine learning approaches for improving condition-based maintenance of naval propulsion plants,” Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment , vol. 230, no. 1, pp. 136–153, 2016
2016
Cited alongside, same era.
2017
Cited alongside, same era.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin, “Large-scale evolution of image classifiers,” in Proceedings of the 34th International Conference on Machine Learning-Volume 70 . JMLR. org, 2017, pp. 2902–2911
2017
Cited alongside, same era.
L. Xie and A. Yuille, “Genetic cnn,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 1379–1388
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Later among the works it cites.
Z. Zhong, J. Yan, W. Wu, J. Shao, and C.-L. Liu, “Practical block-wise neural network architecture generation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 2423–2432
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
J. You, B. Liu, Z. Ying, V. Pande, and J. Leskovec, “Graph convolutional policy network for goal-directed molecular graph generation,” in Advances in neural information processing systems , 2018, pp. 6410–6421
2018
Later among the works it cites.
2019
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2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le, “Regularized evolution for image classifier architecture search,” in Proceedings of the AAAI conference on artificial intelligence , vol. 33, 2019, pp. 4780–4789
2019
Later among the works it cites.
2019
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,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 1294–1303
2019
Later among the works it cites.
Y. Xu, L. Xie, X. Zhang, X. Chen, G.-J. Qi, Q. Tian, and H. Xiong, “Pc-darts: Partial channel connections for memory-efficient architecture search,” in International Conference on Learning Representations , 2019
2019
Later among the works it cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Advances in neural information processing systems , 2019, pp. 5754–5764
2019
Later among the works it cites.
K. Kandasamy, W. Neiswanger, J. Schneider, B. Poczos, and E. P. Xing, “Neural architecture search with bayesian optimisation and optimal transport,” in Advances in Neural Information Processing Systems , 2018, pp. 2016–2025
2025
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