Fetching the paper…
Reading the bibliography…
Deep learning (DL) techniques have penetrated all aspects of our lives and brought us great convenience.
1901
Earlier work this paper cites.
J. Stork, M. Zaefferer, T. Bartz-Beielstein, Improving neuroevolution efficiency by surrogate model-based optimization with phenotypic distance kernels (2019) · 1902
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
1904
Earlier work this paper cites.
Y. He, P. Liu, L. Zhu, Y. Yang, Meta filter pruning to accelerate deep convolutional neural networks (2019) · 1904
Earlier work this paper cites.
1905
Earlier work this paper cites.
H. Zhu, Z. An, C. Yang, K. Xu, E. Zhao, Y. Xu, Eena: Efficient evolution of neural architecture (2019) · 1905
Earlier work this paper cites.
1905
Earlier work this paper cites.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1907
Earlier work this paper cites.
A. Naghizadeh, M. Abavisani, D. N. Metaxas, Greedy autoaugment, arXiv preprint arXiv:1908.00704
1908
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
1909
Earlier work this paper cites.
C. White, W. Neiswanger, Y. Savani, Bananas: Bayesian optimization with neural architectures for neural architecture search (2019) · 1910
Earlier work this paper cites.
1911
Earlier work this paper cites.
1912
Earlier work this paper cites.
doi:10.18653/v1/P19-1185
R. Pasunuru, M. Bansal, Continual and multi-task architecture search , in: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Florence, Italy, 2019, pp. 1911–1922 · 1922
Earlier work this paper cites.
M. G. Kendall, A new measure of rank correlation , Biometrika 30 (1/2) (1938) 81–93. URL http://www.jstor.org/stable/2332226
1938
Earlier work this paper cites.
doi:10.1145/3292500.3330648
H. Jin, Q. Song, X. Hu, Auto-keras: An efficient neural architecture search system , in: A. Teredesai, V. Kumar, Y. Li, R. Rosales, E. Terzi, G. Karypis (Eds.), Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019, ACM, 2019, pp. 1946–1956 · 1956
Earlier work this paper cites.
V. Nekrasov, H. Chen, C. Shen, I. Reid, Architecture search of dynamic cells for semantic video segmentation, in: The IEEE Winter Conference on Applications of Computer Vision, 2020, pp. 1970–1979
1979
Earlier work this paper cites.
N. S. Altman, An introduction to kernel and nearest-neighbor nonparametric regression, The American Statistician 46 (3) (1992) 175–185
1992
Earlier work this paper cites.
R. J. Williams, Simple statistical gradient-following algorithms for connectionist reinforcement learning, Machine learning 8 (3-4) (1992) 229–256
1992
Earlier work this paper cites.
F. Gruau, Cellular encoding as a graph grammar, in: IEEE Colloquium on Grammatical Inference: Theory, Applications & Alternatives, 1993
1993
Earlier work this paper cites.
M. Marcus, G. Kim, M. A. Marcinkiewicz, R. MacIntyre, A. Bies, M. Ferguson, K. Katz, B. Schasberger, The Penn Treebank: Annotating predicate argument structure , in: Human Language Technology: Proceedings of a Workshop held at Plainsboro, New Jersey, March 8-11, 1994, 1994. URL https://www.aclweb.org/anthology/H94-1020
1994
Earlier work this paper cites.
doi:10.3115/981658.981684
D. Yarowsky, Unsupervised word sense disambiguation rivaling supervised methods , in: 33rd Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics, Cambridge, Massachusetts, USA, 1995, pp. 189–196 · 1995
Earlier work this paper cites.
C. Cortes, V. Vapnik, Support-vector networks, Machine learning 20 (3) (1995) 273–297
1995
Earlier work this paper cites.
M. Dash, H. Liu, Feature selection for classification, Intelligent data analysis 1 (1-4) (1997) 131–156
1997
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE 86 (11) (1998) 2278–2324
1998
Earlier work this paper cites.
A. Blum, T. Mitchell, Combining labeled and unlabeled data with co-training, in: Proceedings of the eleventh annual conference on Computational learning theory, ACM, 1998, pp. 92–100
1998
Earlier work this paper cites.
M. J. Pazzani, Constructive induction of cartesian product attributes, in: Feature Extraction, Construction and Selection, Springer, 1998, pp. 341–354
1998
Earlier work this paper cites.
Z. Zheng, A comparison of constructing different types of new feature for decision tree learning, in: Feature Extraction, Construction and Selection, Springer, 1998, pp. 239–255
1998
Earlier work this paper cites.
H. Vafaie, K. De Jong, Evolutionary feature space transformation, in: Feature Extraction, Construction and Selection, Springer, 1998, pp. 307–323
1998
Earlier work this paper cites.
A. Camero, H. Wang, E. Alba, T. Bäck, Bayesian neural architecture search using a training-free performance metric (2020) · 2001
Earlier work this paper cites.
K. O. Stanley, R. Miikkulainen, Evolving neural networks through augmenting topologies, Evolutionary computation 10 (2) (2002) 99–127
2002
Earlier work this paper cites.
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, Smote: synthetic minority over-sampling technique, Journal of artificial intelligence research 16 (2002) 321–357
2002
Earlier work this paper cites.
H. Motoda, H. Liu, Feature selection, extraction and construction, Communication of IICM (Institute of Information and Computing Machinery, Taiwan) Vol 5 (67-72) (2002) 2
2002
Earlier work this paper cites.
R. Luo, X. Tan, R. Wang, T. Qin, E. Chen, T.-Y. Liu, Semi-supervised neural architecture search (2020) · 2002
Earlier work this paper cites.
2002
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
C. E. Rasmussen, Gaussian processes in machine learning, Lecture Notes in Computer Science (2003) 63–71
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
2003
Earlier work this paper cites.
C. Liu, P. Dollár, K. He, R. Girshick, A. Yuille, S. Xie, Are labels necessary for neural architecture search? (2020) · 2003
Earlier work this paper cites.
Y. Zhou, S. Goldman, Democratic co-learning, in: Tools with Artificial Intelligence, 2004. ICTAI 2004. 16th IEEE International Conference on, IEEE, 2004, pp. 594–602
2004
Earlier work this paper cites.
H. Guo, H. L. Viktor, Learning from imbalanced data sets with boosting and data generation: the databoost-im approach, ACM Sigkdd Explorations Newsletter 6 (1) (2004) 30–39
2004
Earlier work this paper cites.
J. Gama, Functional trees, Machine Learning 55 (3) (2004) 219–250
2004
Earlier work this paper cites.
C. He, M. Annavaram, S. Avestimehr, Fednas: Federated deep learning via neural architecture search (2020) · 2004
Earlier work this paper cites.
P. Ren, Y. Xiao, X. Chang, P.-Y. Huang, Z. Li, X. Chen, X. Wang, A comprehensive survey of neural architecture search: Challenges and solutions (2020) · 2006
Earlier work this paper cites.
J. F. Miller, S. L. Smith, Redundancy and computational efficiency in cartesian genetic programming, IEEE Transactions on Evolutionary Computation 10 (2) (2006) 167–174
2006
Earlier work this paper cites.
N. Klyuchnikov, I. Trofimov, E. Artemova, M. Salnikov, M. Fedorov, E. Burnaev, Nas-bench-nlp: Neural architecture search benchmark for natural language processing (2020) · 2006
Earlier work this paper cites.
X. Dong, M. Tan, A. W. Yu, D. Peng, B. Gabrys, Q. V. Le, Autohas: Differentiable hyper-parameter and architecture search (2020) · 2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
2006
Earlier work this paper cites.
B. Collins, J. Deng, K. Li, L. Fei-Fei, Towards scalable dataset construction: An active learning approach, in: European conference on computer vision, Springer, 2008, pp. 86–98
2008
Earlier work this paper cites.
J. F. Miller, S. L. Harding, Cartesian genetic programming, in: Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, ACM, 2008, pp. 2701–2726
2008
Earlier work this paper cites.
doi:10.1109/CVPR.2009.5206848
J. Deng, W. Dong, R. Socher, L. Li, K. Li, F. Li, Imagenet: A large-scale hierarchical image database , in: 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 20-25 June 2009, Miami, Florida, USA, IEEE Computer Society, 2009, pp. 248–255 · 2009
Earlier work this paper cites.
P. Sondhi, Feature construction methods: a survey, sifaka. cs. uiuc. edu 69 (2009) 70–71
2009
Earlier work this paper cites.
D. Roth, K. Small, Interactive feature space construction using semantic information , in: Proceedings of the Thirteenth Conference on Computational Natural Language Learning (CoNLL-2009), Association for Computational Linguistics, Boulder, Colorado, 2009, pp. 66–74. URL https://www.aclweb.org/anthology/W09-1110
2009
Earlier work this paper cites.
M. F. A. Hady, F. Schwenker, Combining committee-based semi-supervised learning and active learning, Journal of Computer Science and Technology 25 (4) (2010) 681–698
2010
Earlier work this paper cites.
J. Y. Hesterman, L. Caucci, M. A. Kupinski, H. H. Barrett, L. R. Furenlid, Maximum-likelihood estimation with a contracting-grid search algorithm, IEEE transactions on nuclear science 57 (3) (2010) 1077–1084
2010
Earlier work this paper cites.
F. Hutter, H. H. Hoos, K. Leyton-Brown, Sequential model-based optimization for general algorithm configuration, in: International conference on learning and intelligent optimization, 2011, pp. 507–523
2011
Earlier work this paper cites.
J. Bergstra, R. Bardenet, Y. Bengio, B. Kégl, Algorithms for hyper-parameter optimization , in: J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. C. N. Pereira, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain, 2011, pp. 2546–2554. URL https://proceedings.neurips.cc/paper/2011/hash/86e8f7ab32cfd12577bc2619bc635690-Abstract.html
2011
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, E. Duchesnay, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research 12 (2011) 2825–2830
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, G. E. Hinton, Imagenet classification with deep convolutional neural networks , in: P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012, pp. 1106–1114. URL https://proceedings.neurips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html
2012
Earlier work this paper cites.
J. Snoek, H. Larochelle, R. P. Adams, Practical bayesian optimization of machine learning algorithms , in: P. L. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012, Lake Tahoe, Nevada, United States, 2012, pp. 2960–2968. URL https://proceedings.neurips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html
2012
Earlier work this paper cites.
J. Bergstra, Y. Bengio, Random search for hyper-parameter optimization, Journal of machine learning research 13 (Feb) (2012) 281–305
2012
Earlier work this paper cites.
J. Domke, Generic methods for optimization-based modeling, in: Artificial Intelligence and Statistics, 2012, pp. 318–326
2012
Earlier work this paper cites.
J. Bergstra, D. Yamins, D. D. Cox, Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures , in: Proceedings of the 30th International Conference on Machine Learning, ICML 2013, Atlanta, GA, USA, 16-21 June 2013, Vol. 28 of JMLR Workshop and Conference Proceedings, JMLR.org, 2013, pp. 115–123. URL http://proceedings.mlr.press/v28/bergstra13.html
2013
Earlier work this paper cites.
doi:10.1109/ICCV.2013.178
X. Chen, A. Shrivastava, A. Gupta, NEIL: extracting visual knowledge from web data , in: IEEE International Conference on Computer Vision, ICCV 2013, Sydney, Australia, December 1-8, 2013, IEEE Computer Society, 2013, pp. 1409–1416 · 2013
Earlier work this paper cites.
J. K. Pugh, K. O. Stanley, Evolving multimodal controllers with hyperneat, in: Proceedings of the 15th annual conference on Genetic and evolutionary computation, ACM, 2013, pp. 735–742
2013
Earlier work this paper cites.
doi:10.1145/2487575.2487629
C. Thornton, F. Hutter, H. H. Hoos, K. Leyton-Brown, Auto-weka: combined selection and hyperparameter optimization of classification algorithms , in: I. S. Dhillon, Y. Koren, R. Ghani, T. E. Senator, P. Bradley, R. Parekh, J. He, R. L. Grossman, R. Uthurusamy (Eds.), The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, Chicago, IL, USA, August 11-14, 2013, ACM, 2013, pp. 847–855 · 2013
Earlier work this paper cites.
Y. Xia, X. Cao, F. Wen, J. Sun, Well begun is half done: Generating high-quality seeds for automatic image dataset construction from web, in: European Conference on Computer Vision, Springer, 2014, pp. 387–400
2014
Earlier work this paper cites.
I. Triguero, J. A. Sáez, J. Luengo, S. García, F. Herrera, On the characterization of noise filters for self-training semi-supervised in nearest neighbor classification, Neurocomputing 132 (2014) 30–41
2014
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, Y. Bengio, Generative adversarial nets , in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, 2014, pp. 2672–2680. URL https://proceedings.neurips.cc/paper/2014/hash/5ca3e9b122f61f8f06494c97b1afccf3-Abstract.html
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, H. Lipson, How transferable are features in deep neural networks? , in: Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger (Eds.), Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada, 2014, pp. 3320–3328. URL https://proceedings.neurips.cc/paper/2014/hash/375c71349b295fbe2dcdca9206f20a06-Abstract.html
2014
Earlier work this paper cites.
K. Swersky, D. Duvenaud, J. Snoek, F. Hutter, M. A. Osborne, Raiders of the lost architecture: Kernels for bayesian optimization in conditional parameter spaces (2014) · 2014
Earlier work this paper cites.
K. Eggensperger, F. Hutter, H. H. Hoos, K. Leyton-Brown, Surrogate benchmarks for hyperparameter optimization., in: MetaSel@ ECAI, 2014, pp. 24–31
2014
Earlier work this paper cites.
C. Wang, Q. Duan, W. Gong, A. Ye, Z. Di, C. Miao, An evaluation of adaptive surrogate modeling based optimization with two benchmark problems, Environmental Modelling & Software 60 (2014) 167–179
2014
Earlier work this paper cites.
doi:10.1007/s11263-015-0816-y
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, L. Fei-Fei, ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision (IJCV) 115 (3) (2015) 211–252 · 2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
N. H. Do, K. Yanai, Automatic construction of action datasets using web videos with density-based cluster analysis and outlier detection, in: Pacific-Rim Symposium on Image and Video Technology, Springer, 2015, pp. 160–172
2015
Earlier work this paper cites.
doi:10.1109/ICCV.2015.168
X. Chen, A. Gupta, Webly supervised learning of convolutional networks , in: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, IEEE Computer Society, 2015, pp. 1431–1439 · 2015
Earlier work this paper cites.
doi:10.1109/ICCV.2015.290
Z. Xu, S. Huang, Y. Zhang, D. Tao, Augmenting strong supervision using web data for fine-grained categorization , in: 2015 IEEE International Conference on Computer Vision, ICCV 2015, Santiago, Chile, December 7-13, 2015, IEEE Computer Society, 2015, pp. 2524–2532 · 2015
Earlier work this paper cites.
doi:10.1145/2723372.2749431
X. Chu, J. Morcos, I. F. Ilyas, M. Ouzzani, P. Papotti, N. Tang, Y. Ye, KATARA: A data cleaning system powered by knowledge bases and crowdsourcing , in: T. K. Sellis, S. B. Davidson, Z. G. Ives (Eds.), Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, Melbourne, Victoria, Australia, May 31 - June 4, 2015, ACM, 2015, pp. 1247–1261 · 2015
Cited alongside, same era.
S. Krishnan, J. Wang, M. J. Franklin, K. Goldberg, T. Kraska, T. Milo, E. Wu, Sampleclean: Fast and reliable analytics on dirty data., IEEE Data Eng. Bull. 38 (3) (2015) 59–75
2015
Cited alongside, same era.
M. Courbariaux, Y. Bengio, J. David, Binaryconnect: Training deep neural networks with binary weights during propagations , in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 3123–3131. URL https://proceedings.neurips.cc/paper/2015/hash/3e15cc11f979ed25912dff5b0669f2cd-Abstract.html
2015
Cited alongside, same era.
A. Mikołajczyk, M. Grochowski, Style transfer-based image synthesis as an efficient regularization technique in deep learning, in: 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR), IEEE, 2019, pp. 42–47
2019
Closest in time.
E. Ma, Nlp augmentation, https://github.com/makcedward/nlpaug (2019)
2019
Closest in time.
doi:10.1109/CVPR.2019.00020
E. D. Cubuk, B. Zoph, D. Mané, V. Vasudevan, Q. V. Le, Autoaugment: Learning augmentation strategies from data , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 113–123 · 2019
Closest in time.
S. Lim, I. Kim, T. Kim, C. Kim, S. Kim, Fast autoaugment , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 6662–6672. URL https://proceedings.neurips.cc/paper/2019/hash/6add07cf50424b14fdf649da87843d01-Abstract.html
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. M. A. Patwary, Prabhat, R. P. Adams, Scalable bayesian optimization using deep neural networks , in: F. R. Bach, D. M. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, Vol. 37 of JMLR Workshop and Conference Proceedings, JMLR.org, 2015, pp. 2171–2180. URL http://proceedings.mlr.press/v37/snoek15.html
2015
Cited alongside, same era.
D. Maclaurin, D. Duvenaud, R. P. Adams, Gradient-based hyperparameter optimization through reversible learning , in: F. R. Bach, D. M. Blei (Eds.), Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, Vol. 37 of JMLR Workshop and Conference Proceedings, JMLR.org, 2015, pp. 2113–2122. URL http://proceedings.mlr.press/v37/maclaurin15.html
2015
Cited alongside, same era.
2015
Cited alongside, same era.
K. Eggensperger, F. Hutter, H. H. Hoos, K. Leyton-Brown, Efficient benchmarking of hyperparameter optimizers via surrogates , in: B. Bonet, S. Koenig (Eds.), Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, USA, AAAI Press, 2015, pp. 1114–1120. URL http://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9993
2015
Cited alongside, same era.
T. Domhan, J. T. Springenberg, F. Hutter, Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves , in: Q. Yang, M. J. Wooldridge (Eds.), Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25-31, 2015, AAAI Press, 2015, pp. 3460–3468. URL http://ijcai.org/Abstract/15/487
2015
Cited alongside, same era.
M. Feurer, A. Klein, K. Eggensperger, J. T. Springenberg, M. Blum, F. Hutter, Efficient and robust automated machine learning , in: C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett (Eds.), Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, 2015, pp. 2962–2970. URL https://proceedings.neurips.cc/paper/2015/hash/11d0e6287202fced83f79975ec59a3a6-Abstract.html
2015
Cited alongside, same era.
F. Chollet, et al., Keras, https://github.com/fchollet/keras (2015)
2015
Cited alongside, same era.
doi:10.1109/CVPR.2016.90
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition , in: 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, IEEE Computer Society, 2016, pp. 770–778 · 2016
Cited alongside, same era.
2019
Closest in time.
D. Ho, E. Liang, X. Chen, I. Stoica, P. Abbeel, Population based augmentation: Efficient learning of augmentation policy schedules , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 2731–2741. URL http://proceedings.mlr.press/v97/ho19b.html
2019
Closest in time.
doi:10.18653/v1/D19-1132
T. Niu, M. Bansal, Automatically learning data augmentation policies for dialogue tasks , in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 1317–1323 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00668
C. Lin, M. Guo, C. Li, X. Yuan, W. Wu, J. Yan, D. Lin, W. Ouyang, Online hyper-parameter learning for auto-augmentation strategy , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 6578–6587 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00138
X. Chen, L. Xie, J. Wu, Q. Tian, Progressive differentiable architecture search: Bridging the depth gap between search and evaluation , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 1294–1303 · 2019
Closest in time.
doi:10.1109/CVPR.2019.00017
C. Liu, L. Chen, F. Schroff, H. Adam, W. Hua, A. L. Yuille, F. Li, Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 82–92 · 2019
Closest in time.
doi:10.1109/CVPR.2019.00293
M. Tan, B. Chen, R. Pang, V. Vasudevan, M. Sandler, A. Howard, Q. V. Le, Mnasnet: Platform-aware neural architecture search for mobile , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 2820–2828 · 2019
Closest in time.
doi:10.1109/CVPR.2019.01099
B. Wu, X. Dai, P. Zhang, Y. Wang, F. Sun, Y. Wu, Y. Tian, P. Vajda, Y. Jia, K. Keutzer, Fbnet: Hardware-aware efficient convnet design via differentiable neural architecture search , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 10734–10742 · 2019
Closest in time.
H. Cai, L. Zhu, S. Han, Proxylessnas: Direct neural architecture search on target task and hardware , in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. URL https://openreview.net/forum?id=HylVB3AqYm
2019
Closest in time.
A. Kwasigroch, M. Grochowski, M. Mikolajczyk, Deep neural network architecture search using network morphism, in: 2019 24th International Conference on Methods and Models in Automation and Robotics (MMAR), IEEE, 2019, pp. 30–35
2019
Closest in time.
M. Tan, Q. V. Le, Efficientnet: Rethinking model scaling for convolutional neural networks , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 6105–6114. URL http://proceedings.mlr.press/v97/tan19a.html
2019
Closest in time.
doi:10.1109/CVPR.2019.00186
X. Dong, Y. Yang, Searching for a robust neural architecture in four GPU hours , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 1761–1770 · 2019
Closest in time.
S. Xie, H. Zheng, C. Liu, L. Lin, SNAS: stochastic neural architecture search , in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. URL https://openreview.net/forum?id=rylqooRqK7
2019
Closest in time.
R. Negrinho, M. R. Gormley, G. J. Gordon, D. Patil, N. Le, D. Ferreira, Towards modular and programmable architecture search , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 13715–13725. URL https://proceedings.neurips.cc/paper/2019/hash/4ab50afd6dcc95fcba76d0fe04295632-Abstract.html
2019
Closest in time.
G. Dikov, J. Bayer, Bayesian learning of neural network architectures , in: K. Chaudhuri, M. Sugiyama (Eds.), The 22nd International Conference on Artificial Intelligence and Statistics, AISTATS 2019, 16-18 April 2019, Naha, Okinawa, Japan, Vol. 89 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 730–738. URL http://proceedings.mlr.press/v89/dikov19a.html
2019
Closest in time.
A. sharpdarts, V. Jain, G. D. Hager, sharpdarts: Faster and more accurate differentiable architecture search, Tech. rep. (2019)
2019
Closest in time.
Y. Geifman, R. El-Yaniv, Deep active learning with a neural architecture search , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 5974–5984. URL https://proceedings.neurips.cc/paper/2019/hash/b59307fdacf7b2db12ec4bd5ca1caba8-Abstract.html
2019
Closest in time.
L. Li, A. Talwalkar, Random search and reproducibility for neural architecture search , in: A. Globerson, R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI 2019, Tel Aviv, Israel, July 22-25, 2019, Vol. 115 of Proceedings of Machine Learning Research, AUAI Press, 2019, pp. 367–377. URL http://proceedings.mlr.press/v115/li20c.html
2019
Closest in time.
M. Feurer, F. Hutter, Hyperparameter Optimization , Springer International Publishing, Cham, 2019, pp. 3–33. URL https://doi.org/10.1007/978-3-030-05318-5_1
2019
Closest in time.
doi:10.1609/aaai.v33i01.33013846
Y. Hu, Y. Yu, W. Tu, Q. Yang, Y. Chen, W. Dai, Multi-fidelity automatic hyper-parameter tuning via transfer series expansion , in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, 2019, pp. 3846–3853 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00661
J. Cui, P. Chen, R. Li, S. Liu, X. Shen, J. Jia, Fast and practical neural architecture search , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 6508–6517 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00378
X. Dong, Y. Yang, One-shot neural architecture search via self-evaluated template network , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 3680–3689 · 2019
Closest in time.
H. Zhou, M. Yang, J. Wang, W. Pan, Bayesnas: A bayesian approach for neural architecture search , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 7603–7613. URL http://proceedings.mlr.press/v97/zhou19e.html
2019
Closest in time.
C. Zhang, M. Ren, R. Urtasun, Graph hypernetworks for neural architecture search , in: 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, OpenReview.net, 2019. URL https://openreview.net/forum?id=rkgW0oA9FX
2019
Closest in time.
doi:10.1609/aaai.v33i01.33013927
R. Istrate, F. Scheidegger, G. Mariani, D. S. Nikolopoulos, C. Bekas, A. C. I. Malossi, TAPAS: train-less accuracy predictor for architecture search , in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, 2019, pp. 3927–3934 · 2019
Closest in time.
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, F. Hutter, Nas-bench-101: Towards reproducible neural architecture search , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 7105–7114. URL http://proceedings.mlr.press/v97/ying19a.html
2019
Closest in time.
Y. Benyahia, K. Yu, K. Bennani-Smires, M. Jaggi, A. C. Davison, M. Salzmann, C. Musat, Overcoming multi-model forgetting , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 594–603. URL http://proceedings.mlr.press/v97/benyahia19a.html
2019
Closest in time.
L. Faes, S. K. Wagner, D. J. Fu, X. Liu, E. Korot, J. R. Ledsam, T. Back, R. Chopra, N. Pontikos, C. Kern, et al., Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study, The Lancet Digital Health 1 (5) (2019) e232–e242
2019
Closest in time.
doi:10.1109/CVPR.2019.00720
G. Ghiasi, T. Lin, Q. V. Le, NAS-FPN: learning scalable feature pyramid architecture for object detection , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 7036–7045 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00675
H. Xu, L. Yao, Z. Li, X. Liang, W. Zhang, Auto-fpn: Automatic network architecture adaptation for object detection beyond classification , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 6648–6657 · 2019
Closest in time.
2019
Closest in time.
Y. Weng, T. Zhou, Y. Li, X. Qiu, Nas-unet: Neural architecture search for medical image segmentation, IEEE Access 7 (2019) 44247–44257
2019
Closest in time.
doi:10.1109/CVPR.2019.00934
V. Nekrasov, H. Chen, C. Shen, I. D. Reid, Fast neural architecture search of compact semantic segmentation models via auxiliary cells , in: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16-20, 2019, Computer Vision Foundation / IEEE, 2019, pp. 9126–9135 · 2019
Closest in time.
W. Bae, S. Lee, Y. Lee, B. Park, M. Chung, K.-H. Jung, Resource optimized neural architecture search for 3d medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 228–236
2019
Closest in time.
D. Yang, H. Roth, Z. Xu, F. Milletari, L. Zhang, D. Xu, Searching learning strategy with reinforcement learning for 3d medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 3–11
2019
Closest in time.
N. Dong, M. Xu, X. Liang, Y. Jiang, W. Dai, E. Xing, Neural architecture search for adversarial medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 828–836
2019
Closest in time.
S. Kim, I. Kim, S. Lim, W. Baek, C. Kim, H. Cho, B. Yoon, T. Kim, Scalable neural architecture search for 3d medical image segmentation, in: International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2019, pp. 220–228
2019
Closest in time.
doi:10.1109/ICCV.2019.00385
R. Quan, X. Dong, Y. Wu, L. Zhu, Y. Yang, Auto-reid: Searching for a part-aware convnet for person re-identification , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 3749–3758 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00332
X. Gong, S. Chang, Y. Jiang, Z. Wang, Autogan: Neural architecture search for generative adversarial networks , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 3223–3233 · 2019
Closest in time.
doi:10.1109/ICCV.2019.00190
T. Saikia, Y. Marrakchi, A. Zela, F. Hutter, T. Brox, Autodispnet: Improving disparity estimation with automl , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 1812–1823 · 2019
Closest in time.
W. Peng, X. Hong, G. Zhao, Video action recognition via neural architecture searching, in: 2019 IEEE International Conference on Image Processing (ICIP), IEEE, 2019, pp. 11–15
2019
Closest in time.
doi:10.1109/ICCV.2019.00188
A. J. Piergiovanni, A. Angelova, A. Toshev, M. S. Ryoo, Evolving space-time neural architectures for videos , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 1793–1802 · 2019
Closest in time.
doi:10.18653/v1/D19-1367
Y. Jiang, C. Hu, T. Xiao, C. Zhang, J. Zhu, Improved differentiable architecture search for language modeling and named entity recognition , in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Hong Kong, China, 2019, pp. 3585–3590 · 2019
Closest in time.
H. Mazzawi, X. Gonzalvo, A. Kracun, P. Sridhar, N. Subrahmanya, I. Lopez-Moreno, H.-J. Park, P. Violette, Improving keyword spotting and language identification via neural architecture search at scale., in: INTERSPEECH, 2019, pp. 1278–1282
2019
Closest in time.
X. Xiao, Z. Wang, S. Rajasekaran, Autoprune: Automatic network pruning by regularizing auxiliary parameters , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 13681–13691. URL https://proceedings.neurips.cc/paper/2019/hash/4efc9e02abdab6b6166251918570a307-Abstract.html
2019
Closest in time.
R. Zhao, W. Luk, Efficient structured pruning and architecture searching for group convolution, in: Proceedings of the IEEE International Conference on Computer Vision Workshops, 2019, pp. 0–0
2019
Closest in time.
X. Dong, Y. Yang, Network pruning via transformable architecture search , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 759–770. URL https://proceedings.neurips.cc/paper/2019/hash/a01a0380ca3c61428c26a231f0e49a09-Abstract.html
2019
Closest in time.
doi:10.1109/ICCV.2019.00850
C. Li, X. Yuan, C. Lin, M. Guo, W. Wu, J. Yan, W. Ouyang, AM-LFS: automl for loss function search , in: 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019, IEEE, 2019, pp. 8409–8418 · 2019
Closest in time.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, OpenAI Blog 1 (2019) 8
2019
Closest in time.
D. Wang, C. Gong, Q. Liu, Improving neural language modeling via adversarial training , in: K. Chaudhuri, R. Salakhutdinov (Eds.), Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA, Vol. 97 of Proceedings of Machine Learning Research, PMLR, 2019, pp. 6555–6565. URL http://proceedings.mlr.press/v97/wang19f.html
2019
Closest in time.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Köpf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, S. Chintala, Pytorch: An imperative style, high-performance deep learning library , in: H. M. Wallach, H. Larochelle, A. Beygelzimer, F. d’Alché-Buc, E. B. Fox, R. Garnett (Eds.), Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada, 2019, pp. 8024–8035. URL https://proceedings.neurips.cc/paper/2019/hash/bdbca288fee7f92f2bfa9f7012727740-Abstract.html
2019
Closest in time.
K. Yu, C. Sciuto, M. Jaggi, C. Musat, M. Salzmann, Evaluating the search phase of neural architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=H1loF2NFwr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00190
Z. Yang, Y. Wang, X. Chen, B. Shi, C. Xu, C. Xu, Q. Tian, C. Xu, CARS: continuous evolution for efficient neural architecture search , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 1826–1835 · 2020
Closest in time.
A. B. Jung, K. Wada, J. Crall, S. Tanaka, J. Graving, C. Reinders, S. Yadav, J. Banerjee, G. Vecsei, A. Kraft, Z. Rui, J. Borovec, C. Vallentin, S. Zhydenko, K. Pfeiffer, B. Cook, I. Fernández, F.-M. De Rainville, C.-H. Weng, A. Ayala-Acevedo, R. Meudec, M. Laporte, et al., imgaug, https://github.com/aleju/imgaug , online; accessed 01-Feb-2020 (2020)
2020
Closest in time.
X. Zhang, Q. Wang, J. Zhang, Z. Zhong, Adversarial autoaugment , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=ByxdUySKvS
2020
Closest in time.
A. Yang, P. M. Esperança, F. M. Carlucci, NAS evaluation is frustratingly hard , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=HygrdpVKvr
2020
Closest in time.
J. Fang, Y. Sun, K. Peng, Q. Zhang, Y. Li, W. Liu, X. Wang, Fast neural network adaptation via parameter remapping and architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=rklTmyBKPH
2020
Closest in time.
doi:10.1109/CVPR42600.2020.01201
C. He, H. Ye, L. Shen, T. Zhang, Milenas: Efficient neural architecture search via mixed-level reformulation , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 11990–11999 · 2020
Closest in time.
Y. Xu, L. Xie, X. Zhang, X. Chen, G. Qi, Q. Tian, H. Xiong, PC-DARTS: partial channel connections for memory-efficient architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=BJlS634tPr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00169
G. Li, G. Qian, I. C. Delgadillo, M. Müller, A. K. Thabet, B. Ghanem, SGAS: sequential greedy architecture search , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 1617–1627 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.00783
M. Zhang, H. Li, S. Pan, X. Chang, S. W. Su, Overcoming multi-model forgetting in one-shot NAS with diversity maximization , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 7806–7815 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.00207
S. You, T. Huang, M. Yang, F. Wang, C. Qian, C. Zhang, Greedynas: Towards fast one-shot NAS with greedy supernet , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 1996–2005 · 2020
Closest in time.
H. Cai, C. Gan, T. Wang, Z. Zhang, S. Han, Once-for-all: Train one network and specialize it for efficient deployment , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=HylxE1HKwS
2020
Closest in time.
J. Mei, Y. Li, X. Lian, X. Jin, L. Yang, A. L. Yuille, J. Yang, Atomnas: Fine-grained end-to-end neural architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=BylQSxHFwr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.01210
S. Hu, S. Xie, H. Zheng, C. Liu, J. Shi, X. Liu, D. Lin, DSNAS: direct neural architecture search without parameter retraining , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 12081–12089 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.01064
J. Fang, Y. Sun, Q. Zhang, Y. Li, W. Liu, X. Wang, Densely connected search space for more flexible neural architecture search , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 10625–10634 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.01298
A. Wan, X. Dai, P. Zhang, Z. He, Y. Tian, S. Xie, B. Wu, M. Yu, T. Xu, K. Chen, P. Vajda, J. E. Gonzalez, Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 12962–12971 · 2020
Closest in time.
X. Dong, Y. Yang, Nas-bench-201: Extending the scope of reproducible neural architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=HJxyZkBKDr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00783
M. Zhang, H. Li, S. Pan, X. Chang, S. W. Su, Overcoming multi-model forgetting in one-shot NAS with diversity maximization , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 7806–7815 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.01079
M. Tan, R. Pang, Q. V. Le, Efficientdet: Scalable and efficient object detection , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 10778–10787 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.01142
J. Guo, K. Han, Y. Wang, C. Zhang, Z. Yang, H. Wu, X. Chen, C. Xu, Hit-detector: Hierarchical trinity architecture search for object detection , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 11402–11411 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.01188
C. Jiang, H. Xu, W. Zhang, X. Liang, Z. Li, SP-NAS: serial-to-parallel backbone search for object detection , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 11860–11869 · 2020
Closest in time.
D. Song, C. Xu, X. Jia, Y. Chen, C. Xu, Y. Wang, Efficient residual dense block search for image super-resolution., in: AAAI, 2020, pp. 12007–12014
2020
Closest in time.
Y. Fu, W. Chen, H. Wang, H. Li, Y. Lin, Z. Wang, Autogan-distiller: Searching to compress generative adversarial networks , in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Vol. 119 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 3292–3303. URL http://proceedings.mlr.press/v119/fu20b.html
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00533
M. Li, J. Lin, Y. Ding, Z. Liu, J. Zhu, S. Han, GAN compression: Efficient architectures for interactive conditional gans , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 5283–5293 · 2020
Closest in time.
doi:10.1109/CVPR42600.2020.00572
C. Gao, Y. Chen, S. Liu, Z. Tan, S. Yan, Adversarialnas: Adversarial neural architecture search for gans , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 5679–5688 · 2020
Closest in time.
M. S. Ryoo, A. J. Piergiovanni, M. Tan, A. Angelova, Assemblenet: Searching for multi-stream neural connectivity in video architectures , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=SJgMK64Ywr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00215
T. Wang, K. Wang, H. Cai, J. Lin, Z. Liu, H. Wang, Y. Lin, S. Han, APQ: joint search for network architecture, pruning and quantization policy , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 2075–2084 · 2020
Closest in time.
E. Real, C. Liang, D. R. So, Q. V. Le, Automl-zero: Evolving machine learning algorithms from scratch , in: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event, Vol. 119 of Proceedings of Machine Learning Research, PMLR, 2020, pp. 8007–8019. URL http://proceedings.mlr.press/v119/real20a.html
2020
Closest in time.
A. Zela, T. Elsken, T. Saikia, Y. Marrakchi, T. Brox, F. Hutter, Understanding and robustifying differentiable architecture search , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=H1gDNyrKDS
2020
Closest in time.
S. KOTYAN, D. V. VARGAS, Is neural architecture search a way forward to develop robust neural networks?, Proceedings of the Annual Conference of JSAI JSAI2020 (2020) 2K1ES203–2K1ES203
2020
Closest in time.
doi:10.1109/CVPR42600.2020.00071
M. Guo, Y. Yang, R. Xu, Z. Liu, D. Lin, When NAS meets robustness: In search of robust architectures against adversarial attacks , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 628–637 · 2020
Closest in time.
Y. Chen, Q. Song, X. Liu, P. S. Sastry, X. Hu, On robustness of neural architecture search under label noise, in: Frontiers in Big Data, 2020
2020
Closest in time.
NNI (Neural Network Intelligence) , 2020. URL https://github.com/microsoft/nni
2020
Closest in time.
Vega , 2020. URL https://github.com/huawei-noah/vega
2020
Closest in time.
D. Lian, Y. Zheng, Y. Xu, Y. Lu, L. Lin, P. Zhao, J. Huang, S. Gao, Towards fast adaptation of neural architectures with meta learning , in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, 2020. URL https://openreview.net/forum?id=r1eowANFvr
2020
Closest in time.
doi:10.1109/CVPR42600.2020.01238
T. Elsken, B. Staffler, J. H. Metzen, F. Hutter, Meta-learning of neural architectures for few-shot learning , in: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, IEEE, 2020, pp. 12362–12372 · 2020
Closest in time.
X. He, S. Wang, X. Chu, S. Shi, J. Tang, X. Liu, C. Yan, J. Zhang, G. Ding, Automated model design and benchmarking of 3d deep learning models for covid-19 detection with chest ct scans (2021) · 2021
Closest in time.