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Recent works (White et al., 2020a; Yan et al., 2020) demonstrate the importance of architecture encodings in Neural Architecture Search (NAS).
Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 1907
Earlier work this paper cites.
Algorithm 97: Shortest path
Floyd, R. W · 1962
Earlier work this paper cites.
Designing neural networks using genetic algorithms
Miller, G. F., Todd, P. M., and Hegde, S. U · 1989
Earlier work this paper cites.
Designing neural networks using genetic algorithms with graph generation system
Kitano, H · 1990
Earlier work this paper cites.
Support vector regression machines
Drucker, H., Burges, C. J. C., Kaufman, L., Smola, A., and Vapnik, V · 1997
Earlier work this paper cites.
Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
Earlier work this paper cites.
Random forests
Breiman, L · 2001
Earlier work this paper cites.
Evolving neural networks through augmenting topologies
Stanley, K. O. and Miikkulainen, R. A · 2002
Earlier work this paper cites.
Npenas: Neural predictor guided evolution for neural architecture search
Wei, C., Niu, C., Tang, Y., and min Liang, J · 2003
Earlier work this paper cites.
Local search is state of the art for neural architecture search benchmarks
White, C., Nolen, S., and Savani, Y · 2005
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E. and Williams, C. K. I · 2006
Earlier work this paper cites.
A surgery of the neural architecture evaluators
Ning, X., Li, W., Zhou, Z., Zhao, T., Zheng, Y., Liang, S., Yang, H., and Wang, Y · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Self-supervised representation learning for evolutionary neural architecture search
Wei, C., Tang, Y., Niu, C., Hu, H., Wang, Y., and Liang, J · 2011
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Scalable bayesian optimization using deep neural networks
Snoek, J., Rippel, O., Swersky, K., Kiros, R., Satish, N., Sundaram, N., Patwary, M., Prabhat, M., and Adams, R · 2015
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XGBoost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F · 2016
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Designing neural network architectures using reinforcement learning
Baker, B., Gupta, O., Naik, N., and Raskar, R · 2017
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Lightgbm: A highly efficient gradient boosting decision tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
Earlier work this paper cites.
Deeparchitect: Automatically designing and training deep architectures
Negrinho, R. and Gordon, G · 2017
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Large-scale evolution of image classifiers
Real, E., Moore, S., Selle, A., Saxena, S., Suematsu, Y. L., Tan, J., Le, Q. V., and Kurakin, A · 2017
Earlier work this paper cites.
Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
Earlier work this paper cites.
Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2017
Earlier work this paper cites.
Accelerating neural architecture search using performance prediction
Baker, B., Gupta, O., Raskar, R., and Naik, N · 2018
Earlier work this paper cites.
Understanding and simplifying one-shot architecture search
Bender, G., Kindermans, P.-J., Zoph, B., Vasudevan, V., and Le, Q · 2018
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
Cited alongside, same era.
Neural architecture search with bayesian optimisation and optimal transport
Kandasamy, K., Neiswanger, W., Schneider, J., Poczos, B., and Xing, E · 2018
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A · 2018
Cited alongside, same era.
Deep contextualized word representations
Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., and Zettlemoyer, L · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
Pham, H., Guan, M. Y., Zoph, B., Le, Q. V., and Dean, J · 2018
Stabilizing differentiable architecture search via perturbation-based regularization
Chen, X. and Hsieh, C.-J · 2020
Later among the works it cites.
NAS-Bench-201: Extending the scope of reproducible neural architecture search
Dong, X. and Yang, Y · 2020
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Bart: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., and Zettlemoyer, L · 2020
Later among the works it cites.
Nsganetv2: Evolutionary multi-objective surrogate-assisted neural architecture search
Lu, Z., Deb, K., Goodman, E., Banzhaf, W., and Boddeti, V. N · 2020
Later among the works it cites.
Semi-supervised neural architecture search
Luo, R., Tan, X., Wang, R., Qin, T., Chen, E., and Liu, T.-Y · 2020
Later among the works it cites.
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Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
Cited alongside, same era.
Unified language model pre-training for natural language understanding and generation
Dong, L., Yang, N., Wang, W., Wei, F., Liu, X., Wang, Y., Gao, J., Zhou, M., and Hon, H.-W · 2019
Cited alongside, same era.
Searching for a robust neural architecture in four gpu hours
Dong, X. and Yang, Y · 2019
Cited alongside, same era.
Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2019
Cited alongside, same era.
Random search and reproducibility for neural architecture search
Li, L. and Talwalkar, A · 2019
Cited alongside, same era.
Ottelander, T. D., Dushatskiy, A., Virgolin, M., and Bosman, P. A · 2020
Later among the works it cites.
Cream of the crop: Distilling prioritized paths for one-shot neural architecture search
Peng, H., Du, H., Yu, H., Li, Q., Liao, J., and Fu, J · 2020
Later among the works it cites.
Designing network design spaces
Radosavovic, I., Kosaraju, R. P., Girshick, R., He, K., and Dollár, P · 2020
Later among the works it cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., and Liu, P. J · 2020
Later among the works it cites.
Neural architecture generator optimization
Ru, B., Esperanca, P., and Carlucci, F · 2020
Later among the works it cites.
Bridging the gap between sample-based and one-shot neural architecture search with bonas
Shi, H., Pi, R., Xu, H., Li, Z., Kwok, J. T., and Zhang, T · 2020
Later among the works it cites.
Nas-bench-301 and the case for surrogate benchmarks for neural architecture search
Siems, J., Zimmer, L., Zela, A., Lukasik, J., Keuper, M., and Hutter, F · 2020
Later among the works it cites.
A semi-supervised assessor of neural architectures
Tang, Y., Wang, Y., Xu, Y., Chen, H., Shi, B., Xu, C., Xu, C., Tian, Q., and Xu, C · 2020
Later among the works it cites.
Alphax: exploring neural architectures with deep neural networks and monte carlo tree search
Wang, L., Zhao, Y., Jinnai, Y., Tian, Y., and Fonseca, R · 2020
Later among the works it cites.
Neural predictor for neural architecture search
Wen, W., Liu, H., Li, H., Chen, Y., Bender, G., and Kindermans, P.-J · 2020
Later among the works it cites.
Weight-sharing neural architecture search: A battle to shrink the optimization gap
Xie, L., Chen, X., et al · 2020
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Does unsupervised architecture representation learning help neural architecture search?
Yan, S., Zheng, Y., Ao, W., Zeng, X., and Zhang, M · 2020
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Understanding and robustifying differentiable architecture search
Zela, A., Elsken, T., Saikia, T., Marrakchi, Y., Brox, T., and Hutter, F · 2020
Later among the works it cites.
Autobss: An efficient algorithm for block stacking style search
Zhang, Y., Zhang, J., and Zhong, Z · 2020
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Pretraining neural architecture search controllers with locality-based self-supervised learning
Choi, K., Choe, M., and Lee, H · 2021
Closest in time.
Contrastive embeddings for neural architectures
Hesslow, D. and Poli, I · 2021
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Smooth variational graph embeddings for efficient neural architecture search
Lukasik, J., Friede, D., Zela, A., Hutter, F., and Keuper, M · 2021
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Interpretable neural architecture search via bayesian optimisation with weisfeiler-lehman kernels
Ru, B., Wan, X., Dong, X., and Osborne, M · 2021
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Bananas: Bayesian optimization with neural architectures for neural architecture search
White, C., Neiswanger, W., and Savani, Y · 2021
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