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Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks.
G. A. Carpenter and S. Grossberg, “The art of adaptive pattern recognition by a self-organizing neural network,” Computer , vol. 21, no. 3, pp. 77–88, 1988
1988
Earlier work this paper cites.
D. J. Felleman and D. C. Van Essen, “Distributed hierarchical processing in the primate cerebral cortex,” Cerebral cortex , vol. 1, no. 1, pp. 1–47, 1991
1991
Earlier work this paper cites.
D. C. Van Essen, C. H. Anderson, and D. J. Felleman, “Information processing in the primate visual system: an integrated systems perspective,” Science , vol. 255, no. 5043, p. 419, 1992
1992
Earlier work this paper cites.
P. J. Grother, “Nist special database 19,” Handprinted forms and characters database, National Institute of Standards and Technology , 1995
1995
Earlier work this paper cites.
G. E. Hinton, P. Dayan, B. J. Frey, and R. M. Neal, “The "Wake-Sleep" algorithm for unsupervised neural networks,” Science , vol. 268, no. 5214, p. 1158, 1995
1995
Earlier work this paper cites.
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE , vol. 86, no. 11, pp. 2278–2324, 1998
1998
Earlier work this paper cites.
K. Louie and M. A. Wilson, “Temporally structured replay of awake hippocampal ensemble activity during rapid eye movement sleep,” Neuron , vol. 29, no. 1, pp. 145–156, 2001
2001
Earlier work this paper cites.
R. A. Chambers, M. N. Potenza, R. E. Hoffman, and W. Miranker, “Simulated apoptosis/neurogenesis regulates learning and memory capabilities of adaptive neural networks,” Neuropsychopharmacology , vol. 29, no. 4, p. 747, 2004
2004
Earlier work this paper cites.
R. Stickgold, “Sleep-dependent memory consolidation,” Nature , vol. 437, no. 7063, pp. 1272–1278, 2005
2005
Earlier work this paper cites.
C. Crick and W. Miranker, “Apoptosis, neurogenesis, and information content in hebbian networks,” Biological cybernetics , vol. 94, no. 1, pp. 9–19, 2006
2006
Earlier work this paper cites.
L. Wiskott, M. J. Rasch, and G. Kempermann, “A functional hypothesis for adult hippocampal neurogenesis: avoidance of catastrophic interference in the dentate gyrus,” Hippocampus , vol. 16, no. 3, pp. 329–343, 2006
2006
Cited alongside, same era.
G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” science , vol. 313, no. 5786, pp. 504–507, 2006
2006
Cited alongside, same era.
R. A. Chambers and S. K. Conroy, “Network modeling of adult neurogenesis: shifting rates of neuronal turnover optimally gears network learning according to novelty gradient,” Journal of cognitive neuroscience , vol. 19, no. 1, pp. 1–12, 2007
2007
Cited alongside, same era.
P. A. Appleby and L. Wiskott, “Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus,” Network: Computation in Neural Systems , vol. 20, no. 3, pp. 137–161, 2009
2009
Cited alongside, same era.
R. Calandra, T. Raiko, M. P. Deisenroth, and F. M. Pouzols, “Learning deep belief networks from non-stationary streams,” in International Conference on Artificial Neural Networks . Springer, 2012, pp. 379–386
2012
Later among the works it cites.
Q. V. Le, “Building high-level features using large scale unsupervised learning,” in Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on . IEEE, 2013, pp. 8595–8598
2013
Later among the works it cites.
A. Krishnamoorthy and D. Menon, “Matrix inversion using cholesky decomposition,” in Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), 2013 . IEEE, 2013, pp. 70–72
2013
Later among the works it cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Later among the works it cites.
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R. Salakhutdinov, “Learning deep generative models,” Ph.D. dissertation, University of Toronto, 2009
2009
Cited alongside, same era.
J. B. Aimone, W. Deng, and F. H. Gage, “Resolving new memories: a critical look at the dentate gyrus, adult neurogenesis, and pattern separation,” Neuron , vol. 70, no. 4, pp. 589–596, 2011
2011
Cited alongside, same era.
P. A. Appleby, G. Kempermann, and L. Wiskott, “The role of additive neurogenesis and synaptic plasticity in a hippocampal memory model with grid-cell like input,” PLoS Comput Biol , vol. 7, no. 1, p. e1001063, 2011
2011
Cited alongside, same era.
J. B. Aimone and F. H. Gage, “Modeling new neuron function: a history of using computational neuroscience to study adult neurogenesis,” European Journal of Neuroscience , vol. 33, no. 6, pp. 1160–1169, 2011
2011
Cited alongside, same era.
M. F. Carr, S. P. Jadhav, and L. M. Frank, “Hippocampal replay in the awake state: a potential substrate for memory consolidation and retrieval,” Nature neuroscience , vol. 14, no. 2, pp. 147–153, 2011
2011
Cited alongside, same era.
D. C. Cireşan, U. Meier, and J. Schmidhuber, “Transfer learning for latin and chinese characters with deep neural networks,” in Neural Networks (IJCNN), The 2012 International Joint Conference on . IEEE, 2012, pp. 1–6
2012
Cited alongside, same era.
J. B. Aimone, Y. Li, S. W. Lee, G. D. Clemenson, W. Deng, and F. H. Gage, “Regulation and function of adult neurogenesis: from genes to cognition,” Physiological reviews , vol. 94, no. 4, pp. 991–1026, 2014
2014
Later among the works it cites.
2014
Later among the works it cites.
C. Kandaswamy, L. M. Silva, L. A. Alexandre, J. M. Santos, and J. M. de Sá, “Improving deep neural network performance by reusing features trained with transductive transference,” in International Conference on Artificial Neural Networks . Springer, 2014, pp. 265–272
2014
Later among the works it cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature , vol. 521, no. 7553, pp. 436–444, 2015
2015
Later among the works it cites.
J. Schmidhuber, “Deep learning in neural networks: An overview,” Neural networks , vol. 61, pp. 85–117, 2015
2015
Later among the works it cites.