Using feature grouping as a stochastic regularizer for high-dimensional noisy data, 2018. ,

Adaptive dropout for training deep neural networks, Adv. Neural. Inform. Process Syst, vol.26, pp.3084-3092, 2013. ,

Understanding dropout, Adv. Neural. Inform. Process Syst, vol.26, pp.2814-2822, 2013. ,

Possible principles underlying the transformations of sensory messages. Sensory Communication, Contributions: Contributions, vol.217, 1959. ,

Representation learning: a review and new perspectives, 2013. ,

Training with noise is equivalent to tikhonov regularization, Neural computation, vol.7, issue.1, pp.108-116, 1995. ,

Evaluation and comparison of clustering algorithms in analyzing es cell gene expression data, Statistica Sinica, pp.241-262, 2002. ,

Reducing overfitting in deep networks by decorrelating representations, 2015. ,

Natural neural networks, Advances in Neural Information Processing Systems, pp.2071-2079, 2015. ,

Improved regularization of convolutional neural networks with cutout, 2017. ,

Ensemble methods in machine learning, International workshop on multiple classifier systems, pp.1-15, 2000. ,

A theoretically grounded application of dropout in recurrent neural networks, Advances in neural information processing systems, pp.1019-1027, 2016. ,

, Computational statistics, vol.308, 2009.

Competitive baseline methods set new standards for the nips 2003 feature selection benchmark, Pattern recognition letters, vol.28, issue.12, pp.1438-1444, 2007. ,

Second order derivatives for network pruning: Optimal brain surgeon, Advances in neural information processing systems, pp.164-171, 1993. ,

Surprising properties of dropout in deep networks, The Journal of Machine Learning Research, vol.18, issue.1, pp.7284-7311, 2017. ,

Improving neural networks by preventing co-adaptation of feature detectors, 2012. ,

Independent component analysis: recent advances, Phil. Trans. R. Soc. A, vol.371, p.20110534, 1984. ,

Batch normalization: Accelerating deep network training by reducing internal covariate shift, Proceedings of the 32Nd International Conference on International Conference on Machine Learning, vol.37, pp.448-456, 2015. ,

ImageNet classification with deep convolutional neural networks, Communications of the ACM, vol.60, issue.6, pp.84-90, 2017. ,

Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy, Machine learning, vol.51, issue.2, pp.181-207, 2003. ,

Optimal brain damage, Advances in neural information processing systems, pp.598-605, 1990. ,

On the generation of correlated artificial binary data, 1998. ,

Learning deep architectures via generalized whitened neural networks, International Conference on Machine Learning, pp.2238-2246, 2017. ,

A Bayesian encourages dropout, 2014. ,

Group invariant scattering, Comm. Pure Appl. Math, vol.65, issue.10, pp.1331-1398, 2012. ,

Diversity networks, 2016. ,

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans. Pattern Anal. Mach. Intell, vol.27, issue.8, pp.1226-1238, 2005. ,

A comparison of methods for simulating correlated binary variables with specified marginal means and correlations, Journal of Statistical Computation and Simulation, vol.84, issue.11, pp.2441-2452, 2014. ,

Regularizing cnns with locally constrained decorrelations, 2016. ,

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis, Journal of computational and applied mathematics, vol.20, pp.53-65, 1987. ,

Dropout: a simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014. ,

Deep Learning and the Information Bottleneck Principle, 2015. ,

Efficient Object Localization Using Convolutional Networks, 2014. ,

Dropout training as adaptive regularization, Adv. Neural. Inform. Process Syst, vol.26, pp.351-359, 2013. ,