Fetching the paper…
Reading the bibliography…
Deep neural networks have been one of the dominant machine learning approaches in recent years.
ome methods of speeding up the convergence of iteration methods
B. Polyak · 1964
Earlier work this paper cites.
A method for unconstrained convex minimization problem with the rate of convergence
Y. Nesterov · 1983
Earlier work this paper cites.
Applications of the method of multipliers to variational inequalities
D. Gabay · 1983
Earlier work this paper cites.
Combinations of genetic algorithms and neural networks: A survey of the state of the art
J. Schaffer, D. Whitley, and L. Eshelman · 1992
Earlier work this paper cites.
Constructive algorithms for structure learning feedforward nerual networks for regression problems
T. Kwok and D. Yeung · 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.
Nonlinear Programming
D. Bertsekas · 1999
Earlier work this paper cites.
Tuning of the structure and parameters of a neural network using an improved genetic algorithm
H. Lam, F. Leung, and P. Tam · 2003
Earlier work this paper cites.
A new strategy for adaptively constructing multiplayer feedforward neural networks
L. Ma and K. Khorasani · 2003
Earlier work this paper cites.
A fast iterative shrinkage thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
Earlier work this paper cites.
Learning fast approximations of sparse coding
K. Gregor and Y. LeCun · 2010
Earlier work this paper cites.
Understanding the difficulty of training deep feedforward neural networks
X. Glorot and Y. Bengio · 2010
Earlier work this paper cites.
Linearized alternating direction method with adaptive penalty for low-rank representation
Z. Lin, R. Liu, , and Z. Su · 2011
Earlier work this paper cites.
ImageNet classification with deep convolutional neural networks
A. Krzhevsky, I. Sutshever, and G. Hinton · 2012
Cited alongside, same era.
Generative neuroevolution for deep learning
P. Verbancsics and J. Harguess · 2013
Cited alongside, same era.
Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
J. Bergstra, D. Yamins, and D. Cox · 2013
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
Cited alongside, same era.
Fast R-CNN
R. Girshick · 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
Maximal sparsity with deep networks?
B. Xin, Y. Wang, W. Gao, B. Wang, and D. Wipf · 2016
Later among the works it cites.
Reconnet: Non-iterative reconstruction of images from compressively sensed mmeasuremets
K. Kulkarni, S. Lohit, P. Turaga, R. Kerviche, and A. Ashok · 2016
Later among the works it cites.
Deep admm-net for compressive sensing mri
Y. Yang, J. Sun, H. Li, and Z. Xu · 2016
Later among the works it cites.
Deep learning with s-shaped rectified linear activation units
X. Jin, C. Xu, J. Feng, Y. Wei, J. Xiong, and S. Yan · 2016
Later among the works it cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. van der Maaten, and K. Weinberger · 2017
Later among the works it cites.
AdaNet: Adaptive structure learning of artificial nerual networks
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, and S. Yang · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Speeding up automatic hyperparameter optimization of deep neural networks by extrapolation of learning curves
T. Domhan, J. Springenberg, and F. Hutter · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Cited alongside, same era.
Faster R-CNN: Towards real-time object detection with region proposal networks
S. Ren, R. Girshick K. He, and J. Sun · 2016
Cited alongside, same era.
Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
Cited alongside, same era.
Neural architecture search with reinforcement learning
B. Zoph and Q. Le · 2017
Later among the works it cites.
J. Zhang and B. Ghanem · 2017
Later among the works it cites.
Recurrent inference machines for solving inverse problems
P. Putzky and M. Welling · 2017
Later among the works it cites.
Deep convolutional neural networks with merge-and-run mappings
L. Zhao, J. Wang, X. Li, Z. Tu, and W. Zeng · 2017
Later among the works it cites.
Convolutional neural networks with alternately updated clique
Y. Yang, Z. Zhong, T. Shen, and Z. Lin · 2018
Closest in time.
Practical block-wise neural network architecture generation
Z. Zhong, J. Yan, W. Wu, J. Shao, and C. Liu · 2018
Closest in time.