Fetching the paper…
Reading the bibliography…
We investigate the capacity control provided by dropout in various machine learning problems.
Rademacher and gaussian complexities: Risk bounds and structural results
Bartlett, P. L. and Mendelson, S · 2002
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., et al · 2009
Earlier work this paper cites.
Collaborative filtering in a non-uniform world: Learning with the weighted trace norm
Srebro, N. and Salakhutdinov, R. R · 2010
Earlier work this paper cites.
Optimistic rates for learning with a smooth loss
Srebro, N., Sridharan, K., and Tewari, A · 2010
Earlier work this paper cites.
Learning with the weighted trace-norm under arbitrary sampling distributions
Foygel, R., Shamir, O., Srebro, N., and Salakhutdinov, R. R · 2011
Earlier work this paper cites.
Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. R · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Understanding dropout
Baldi, P. and Sadowski, P. J · 2013
Earlier work this paper cites.
Improving deep neural networks for lvcsr using rectified linear units and dropout
Dahl, G. E., Sainath, T. N., and Hinton, G. E · 2013
Earlier work this paper cites.
A pac-bayesian tutorial with a dropout bound
McAllester, D · 2013
Earlier work this paper cites.
Dropout training as adaptive regularization
Wager, S., Wang, S., and Liang, P. S · 2013
Earlier work this paper cites.
Regularization of neural networks using dropconnect
Wan, L., Zeiler, M., Zhang, S., Le Cun, Y., and Fergus, R · 2013
Earlier work this paper cites.
Fast dropout training
Wang, S. and Manning, C · 2013
Earlier work this paper cites.
A fast and accurate dependency parser using neural networks
Chen, D. and Manning, C · 2014
Earlier work this paper cites.
A convolutional neural network for modelling sentences
Kalchbrenner, N., Grefenstette, E., and Blunsom, P · 2014
Cited alongside, same era.
Dropout improves recurrent neural networks for handwriting recognition
Pham, V., Bluche, T., Kermorvant, C., and Louradour, J · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Cited alongside, same era.
Deeppose: Human pose estimation via deep neural networks
Toshev, A. and Szegedy, C · 2014
Cited alongside, same era.
Altitude training: Strong bounds for single-layer dropout
Wager, S., Fithian, W., Wang, S., and Liang, P. S · 2014
Cited alongside, same era.
On the inductive bias of dropout
Helmbold, D. P. and Long, P. M · 2015
Brain tumor segmentation with deep neural networks
Havaei, M., Davy, A., Warde-Farley, D., Biard, A., Courville, A., Bengio, Y., Pal, C., Jodoin, P.-M., and Larochelle, H · 2017
Later among the works it cites.
Surprising properties of dropout in deep networks
Helmbold, D. P. and Long, P. M · 2017
Later among the works it cites.
A pac-bayesian approach to spectrally-normalized margin bounds for neural networks
Neyshabur, B., Bhojanapalli, S., and Srebro, N · 2017
Later among the works it cites.
On the relationship between dropout and equiangular tight frames
Bank, D. and Giryes, R · 2018
Later among the works it cites.
Dropout as a low-rank regularizer for matrix factorization
Cavazza, J., Haeffele, B. D., Lane, C., Morerio, P., Murino, V., and Vidal, R · 2018
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.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
Cited alongside, same era.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
Dropout rademacher complexity of deep neural networks
Gao, W. and Zhou, Z.-H · 2016
Cited alongside, same era.
The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2016
Cited alongside, same era.
Improved dropout for shallow and deep learning
Li, Z., Gong, B., and Yang, T · 2016
Cited alongside, same era.
Stacked attention networks for image question answering
Yang, Z., He, X., Gao, J., Deng, L., and Smola, A · 2016
Cited alongside, same era.
Size-independent sample complexity of neural networks
Golowich, N., Rakhlin, A., and Shamir, O · 2018
Later among the works it cites.
Algorithmic regularization in over-parameterized matrix sensing and neural networks with quadratic activations
Li, Y., Ma, T., and Zhang, H · 2018
Later among the works it cites.
On the implicit bias of dropout
Mianjy, P., Arora, R., and Vidal, R · 2018
Later among the works it cites.
Foundations of machine learning
Mohri, M., Rostamizadeh, A., and Talwalkar, A · 2018
Later among the works it cites.
Dropout training, data-dependent regularization, and generalization bounds
Mou, W., Zhou, Y., Gao, J., and Wang, L · 2018
Later among the works it cites.
Towards understanding the role of over-parametrization in generalization of neural networks
Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N · 2018
Later among the works it cites.
High-dimensional probability: An introduction with applications in data science , volume 47
Vershynin, R · 2018
Later among the works it cites.
Adaptive dropout with rademacher complexity regularization
Zhai, K. and Wang, H · 2018
Later among the works it cites.
On dropout and nuclear norm regularization
Mianjy, P. and Arora, R · 2019
Later among the works it cites.