Fetching the paper…
Reading the bibliography…
Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks.
Y. LeCun, J. S. Denker, S. A. Solla, R. E. Howard, and L. D. Jackel, “Optimal brain damage.” in
1989
Earlier work this paper cites.
G. M. S. Peter J. Angeline and J. B. Pollack, “An Evolutionary Algorithm that Constructs Recurrent Neural Networks,”
1994
Earlier work this paper cites.
K. O. Stanley and R. Miikkulainen, “Evolving neural networks through augmenting topologies,”
2002
Earlier work this paper cites.
K. O. Stanley, B. D. Bryant, and R. Miikkulainen, “Real-time neuroevolution in the NERO video game,”
2005
Earlier work this paper cites.
J. Gauci and K. O. Stanley, “Generating Large-Scale Neural Networks Through Discovering Geometric Regularities,” 2007
2007
Earlier work this paper cites.
Y. Bengio, “Learning deep architectures for ai,”
2009
Earlier work this paper cites.
T. Liu, Z. Yuan, J. Sun, J. Wang, N. Zheng, X. Tang, and H.-Y. Shum, “Learning to detect a salient object,”
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Earlier work this paper cites.
G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath
2012
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in
2013
Cited alongside, same era.
C. Farabet, C. Couprie, L. Najman, and Y. LeCun, “Learning hierarchical features for scene labeling,”
2013
Cited alongside, same era.
J. J. Tompson, A. Jain, Y. LeCun, and C. Bregler, “Joint training of a convolutional network and a graphical model for human pose estimation,” in
2014
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,”
2014
Cited alongside, same era.
2014
Cited alongside, same era.
R. K. Srivastava, K. Greff, and J. Schmidhuber, “Training very deep networks,” in
2015
Later among the works it cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
S. Han, J. Pool, J. Tran, and W. Dally, “Learning both weights and connections for efficient neural network,” in
2015
Later among the works it cites.
2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”
2015
Cited alongside, same era.
2015
Cited alongside, same era.
D. Moran, R. Softley, and E. J. Warrant, “The energetic cost of vision and the evolution of eyeless mexican cavefish,”
2015
Later among the works it cites.
G. Li and Y. Yu, “Visual saliency based on multiscale deep features,” in
2015
Later among the works it cites.
S. S. Tirumala, S. Ali, and C. P. Ramesh, “Evolving deep neural networks: A new prospect,” 2016
2016
Closest in time.