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Finding a well-performing architecture is often tedious for both DL practitioners and researchers, leading to tremendous interest in the automation of this task by means of neural architecture search (NAS).
Auto-DeepLab: Hierarchical neural architecture search for semantic image segmentation
C. Liu, L. Chen, F. Schroff, H. Adam, W. Hua, A. Yuille, and L. Fei-Fei · 1901
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Wavelab and reproducible research
J. Buckheit and D. Donoho · 1995
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Confirmation bias: A ubiquitous phenomenon in many guises
R. Nickerson · 1998
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Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
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Towards an empirical foundation for assessing bayesian optimization of hyperparameters
K. Eggensperger, M. Feurer, F. Hutter, J. Bergstra, J. Snoek, H. Hoos, and K. Leyton-Brown · 2013
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Aclib: A benchmark library for algorithm configuration
F. Hutter, M. López-Ibáñez, C. Fawcett, M. Lindauer, H. Hoos, K. Leyton-Brown, and T. Stützle · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Efficient benchmarking of hyperparameter optimizers via surrogates
K. Eggensperger, F. Hutter, H. Hoos, and K. Leyton-Brown · 2015
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ASlib: A benchmark library for algorithm selection
B. Bischl, P. Kerschke, L. Kotthoff, M. Lindauer, Y. Malitsky, A. Fréchette, H. Hoos, F. Hutter, K. Leyton-Brown, K. Tierney, and J. Vanschoren · 2016
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Improved regularization of convolutional neural networks with cutout
T. Devries and G. Taylor · 2017
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Openai baselines, 2017
P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, Y. Wu, and P. Zhokhov · 2017
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Meta-assessment of bias in science
D. Fanelli, R. Costas, and J. Ioannidis · 2017
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Shake-shake regularization
X. Gastaldi · 2017
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Tensorforce: a tensorflow library for applied reinforcement learning
A. Kuhnle, M. Schaarschmidt, and Kai K. Fricke · 2017
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Sgdr: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Deeparchitect: Automatically designing and training deep architectures
R. Negrinho and G. Gordon · 2017
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Mixup: Beyond empirical risk minimization
H. Zhang, M. Cissé, Y. Dauphin, and D. Lopez-Paz · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. Le · 2017
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Searching for efficient multi-scale architectures for dense image prediction
L. Chen, M. Collins, Y. Zhu, G. Papandreou, B. Zoph, F. Schroff, H. Adam, and J. Shlens · 2018
Cited alongside, same era.
Proceedings of the 35th International Conference on Machine Learning (ICML) , volume 80 of JMLR Workshop and Conference Proceedings , 2018. JMLR.org
J. Dy and A. Krause, editors · 2018
Cited alongside, same era.
Efficient benchmarking of algorithm configurators via model-based surrogates
K. Eggensperger, M. Lindauer, H. Hoos, F. Hutter, and K Leyton-Brown · 2018
Cited alongside, same era.
Sigmoid-weighted linear units for neural network function approximation in reinforcement learning
S. Elfwing, E. Uchibe, and K. Doya · 2018
Cited alongside, same era.
Deep reinforcement learning that matters
P. Henderson, R.t Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2018
Cited alongside, same era.
Random search and reproducibility for neural architecture search
L. Li and A. Talwalkar · 2019
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S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim · 2019
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Decoupled weight decay regularization
I. Loshchilov and F. Hutter · 2019
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Keras Tuner
T. O’Malley, E. Bursztein, J. Long, F. Chollet, H. Jin, L. Invernizzi, et al · 2019
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Learning to design RNA
F. Runge, D. Stoll, S. Falkner, and F. Hutter · 2019
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AutoDispNet: Improving disparity estimation with AutoML
T. Saikia, Y. Marrakchi, A. Zela, F. Hutter, and T. Brox · 2019
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Predicting the computational cost of deep learning models
D. Justus, J. Brennan, S. Bonner, and A. McGough · 2018
Cited alongside, same era.
RLlib: Abstractions for distributed reinforcement learning
E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. Jordan, and I. Stoica · 2018
Cited alongside, same era.
Fast neural architecture search of compact semantic segmentation models via auxiliary cells
V. Nekrasov, H. Chen, C. Shen, and I. Reid · 2018
Cited alongside, same era.
Efficient neural architecture search via parameter sharing
H. Pham, M. Guan, B. Zoph, Q. Le, and J. Dean · 2018
Cited alongside, same era.
Exploiting the potential of standard convolutional autoencoders for image restoration by evolutionary search
M. Suganuma, M. Ozay, and T. Okatani · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. Le · 2018
Cited alongside, same era.
Analysis of dawnbench, a time-to-accuracy machine learning performance benchmark
C. Coleman, D. Kang, D. Narayanan, L. Nardi, T. Zhao, J. Zhang, P. Bailis, K. Olukotun, C. Ré, and M. Zaharia · 2019
Cited alongside, same era.
Evaluating the search phase of neural architecture search
C. Sciuto, K. Yu, M. Jaggi, C. Musat, and M. Salzmann · 2019
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D. So, C. Liang, and Q. Le · 2019
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Exploring randomly wired neural networks for image recognition
S. Xie, A. Kirillov, R. Girshick, and K. He · 2019
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NAS-Bench-101: Towards reproducible neural architecture search
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter · 2019
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NAS-Bench-201: extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
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Autogluon-tabular: Robust and accurate automl for structured data
N. Erickson, J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li, and A. Smola · 2020
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NAS-Bench-NLP: neural architecture search benchmark for natural language processing
N. Klyuchnikov, I. Trofimov, E. Artemova, M. Salnikov, M. Fedorov, and E. Burnaev · 2020
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A surgery of the neural architecture evaluators
X. Ning, W. Li, Z. Zhou, T. Zhao, Y. Zheng, S. Liang, H. Yang, and Y. Wang · 2020
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Archai, 2020
S. Shah · 2020
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NAS-Bench-301 and the case for surrogate benchmarks for neural architecture search
J. Siems, L. Zimmer, A. Zela, J. Lukasik, M. Keuper, and F. Hutter · 2020
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NAS evaluation is frustratingly hard
A. Yang, P. Esperança, and F. Carlucci · 2020
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Auto-pytorch tabular: Multi-fidelity metalearning for efficient and robust autodl
L. Zimmer, M. Lindauer, and F. Hutter · 2020
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