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Developing neural network image classification models often requires significant architecture engineering.
A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position
K. Fukushima · 1980
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
R. J. Williams · 1992
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Learning to learn using gradient descent
S. Hochreiter, A. Younger, and P. Conwell · 2001
Earlier work this paper cites.
Modeling systems with internal state using evolino
D. Wierstra, F. J. Gomez, and J. Schmidhuber · 2005
Earlier work this paper cites.
Neuroevolution: from architectures to learning
D. Floreano, P. Dürr, and C. Mattiussi · 2008
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
A high-throughput screening approach to discovering good forms of biologically inspired visual representation
N. Pinto, D. Doukhan, J. J. DiCarlo, and D. D. Cox · 2009
Earlier work this paper cites.
A hypercube-based encoding for evolving large-scale neural networks
K. O. Stanley, D. B. D’Ambrosio, and J. Gauci · 2009
Earlier work this paper cites.
Metalearning
T. Schaul and J. Schmidhuber · 2010
Earlier work this paper cites.
Algorithms for hyper-parameter optimization
J. Bergstra, R. Bardenet, Y. Bengio, and B. Kégl · 2011
Earlier work this paper cites.
Random search for hyper-parameter optimization
J. Bergstra and Y. Bengio · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Practical Bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Earlier work this paper cites.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. D. Cox · 2013
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
An empirical exploration of recurrent network architectures
R. Jozefowicz, W. Zaremba, and I. Sutskever · 2015
Earlier work this paper cites.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
Earlier work this paper cites.
Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Scalable Bayesian optimization using deep neural networks
J. Snoek, O. Rippel, K. Swersky, R. Kiros, N. Satish, N. Sundaram, M. Patwary, M. Ali, R. P. Adams, et al · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Cited alongside, same era.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Cited alongside, same era.
Designing neural network architectures using reinforcement learning
Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
C. Finn, P. Abbeel, and S. Levine · 2017
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Shake-shake regularization of 3-branch residual networks
X. Gastaldi · 2017
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Hypernetworks
D. Ha, A. Dai, and Q. V. Le · 2017
Closest in time.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam · 2017
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B. Baker, O. Gupta, N. Naik, and R. Raskar · 2016
Cited alongside, same era.
Revisiting distributed synchronous sgd
J. Chen, R. Monga, S. Bengio, and R. Jozefowicz · 2016
Cited alongside, same era.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Identity mappings in deep residual networks
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Deep networks with stochastic depth
G. Huang, Y. Sun, Z. Liu, D. Sedra, and K. Weinberger · 2016
Cited alongside, same era.
Fractalnet: Ultra-deep neural networks without residuals
G. Larsson, M. Maire, and G. Shakhnarovich · 2016
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun · 2017
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Densely connected convolutional networks
G. Huang, Z. Liu, and K. Q. Weinberger · 2017
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Speed/accuracy trade-offs for modern convolutional object detectors
J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara, A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al · 2017
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Learning to optimize neural nets
K. Li and J. Malik · 2017
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Feature pyramid networks for object detection
T.-Y. Lin, P. Dollár, R. Girshick, K. He, B. Hariharan, and S. Belongie · 2017
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Focal loss for dense object detection
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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R. Miikkulainen, J. Liang, E. Meyerson, A. Rawal, D. Fink, O. Francon, B. Raju, A. Navruzyan, N. Duffy, and B. Hodjat · 2017
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DeepArchitect: Automatically designing and training deep architectures
R. Negrinho and G. Gordon · 2017
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Optimization as a model for few-shot learning
S. Ravi and H. Larochelle · 2017
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Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, Q. Le, and A. Kurakin · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Learned optimizers that scale and generalize
O. Wichrowska, N. Maheswaranathan, M. W. Hoffman, S. G. Colmenarejo, M. Denil, N. de Freitas, and J. Sohl-Dickstein · 2017
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L. Xie and A. Yuille · 2017
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Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He · 2017
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Polynet: A pursuit of structural diversity in very deep networks
X. Zhang, Z. Li, C. C. Loy, and D. Lin · 2017
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Shufflenet: An extremely efficient convolutional neural network for mobile devices
X. Zhang, X. Zhou, L. Mengxiao, and J. Sun · 2017
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Neural architecture search with reinforcement learning
B. Zoph and Q. V. Le · 2017
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