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Neural Architecture Search (NAS) is a promising and rapidly evolving research area.
Bagging predictors
L. Breiman · 1996
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Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
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A language modeling approach to information retrieval
J. M. Ponte and W. B. Croft · 1998
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Placing search in context: The concept revisited
L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan, G. Wolfman, and E. Ruppin · 2001
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A neural probabilistic language model
Y. Bengio, R. Ducharme, P. Vincent, and C. Jauvin · 2003
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Exploring network structure, dynamics, and function using networkx
A. Hagberg, P. Swart, and D. S Chult · 2008
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Recurrent neural network based language model
T. Mikolov, M. Karafiát, L. Burget, J. Černockỳ, and S. Khudanpur · 2010
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Sequential model-based optimization for general algorithm configuration
F. Hutter, H. H. Hoos, and K. Leyton-Brown · 2011
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. D. Cox · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Don’t count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors
M. Baroni, G. Dinu, and G. Kruszewski · 2014
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On the properties of neural machine translation: Encoder-decoder approaches
K. Cho, B. Van Merriënboer, D. Bahdanau, and Y. Bengio · 2014
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An exact graph edit distance algorithm for solving pattern recognition problems
Z. Abu-Aisheh, R. Raveaux, J.-Y. Ramel, and P. Martineau · 2015
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Simlex-999: Evaluating semantic models with (genuine) similarity estimation
F. Hill, R. Reichart, and A. Korhonen · 2015
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An empirical exploration of recurrent network architectures
R. Jozefowicz, W. Zaremba, and I. Sutskever · 2015
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Evaluation methods for unsupervised word embeddings
T. Schnabel, I. Labutov, D. Mimno, and T. Joachims · 2015
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Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
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Lstm: A search space odyssey
K. Greff, R. K. Srivastava, J. Koutník, B. R. Steunebrink, and J. Schmidhuber · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Exploring the limits of language modeling
R. Jozefowicz, O. Vinyals, M. Schuster, N. Shazeer, and Y. Wu · 2016
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Pointer sentinel mixture models
S. Merity, C. Xiong, J. Bradbury, and R. Socher · 2016
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Densely connected attention propagation for reading comprehension
Y. Tay, A. T. Luu, S. C. Hui, and J. Su · 2018
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Glue: A multi-task benchmark and analysis platform for natural language understanding
A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, and S. Bowman · 2018
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Densely connected cnn with multi-scale feature attention for text classification
S. Wang, M. Huang, and Z. Deng · 2018
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Electra: Pre-training text encoders as discriminators rather than generators
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
J. D. M.-W. C. Kenton and L. K. Toutanova · 2019
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Tabular benchmarks for joint architecture and hyperparameter optimization
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B. Zoph and Q. V. Le · 2016
Cited alongside, same era.
Massive exploration of neural machine translation architectures
D. Britz, A. Goldie, M.-T. Luong, and Q. Le · 2017
Cited alongside, same era.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Cited alongside, same era.
Hyperband: A novel bandit-based approach to hyperparameter optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2017
Cited alongside, same era.
Regularizing and optimizing lstm language models
S. Merity, N. S. Keskar, and R. Socher · 2017
Cited alongside, same era.
graph2vec: Learning distributed representations of graphs
A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and S. Jaiswal · 2017
Cited alongside, same era.
Using the output embedding to improve language models
O. Press and L. Wolf · 2017
Cited alongside, same era.
A. Klein and F. Hutter · 2019
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Random search and reproducibility for neural architecture search
L. Li and A. Talwalkar · 2019
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Best practices for scientific research on neural architecture search
M. Lindauer and F. Hutter · 2019
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Exploring the limits of transfer learning with a unified text-to-text transformer
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu · 2019
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Regularized evolution for image classifier architecture search
E. Real, A. Aggarwal, Y. Huang, and Q. V. Le · 2019
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Nas evaluation is frustratingly hard
A. Yang, P. M. Esperança, and F. M. Carlucci · 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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https://github.com/salesforce/awd-lstm-lm
Lstm and qrnn language model toolkit · 2020
Closest in time.
Nas-bench-201: Extending the scope of reproducible neural architecture search
X. Dong and Y. Yang · 2020
Closest in time.
Nas-bench-1shot1: Benchmarking and dissecting one-shot neural architecture search
A. Zela, J. Siems, and F. Hutter · 2020
Closest in time.