QBDC: Query by dropout committee for training deep supervised architecture

Query by committee

Seung, H. S., Opper, M., and Sompolinsky, H · 1992

Earlier work this paper cites.

Selective sampling using the query by committee algorithm

Freund, Yoav, Seung, H. Sebastian, Shamir, Eli, and Tishby, Naftali · 1997

Earlier work this paper cites.

Gradient-based learning applied to document recognition

LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998

Earlier work this paper cites.

Diverse ensembles for active learning

Melville, Prem and Mooney, Raymond J · 2004

Earlier work this paper cites.

Greedy layer-wise training of deep networks

Bengio, Yoshua, Lamblin, Pascal, Popovici, Dan, and Larochelle, Hugo · 2007

Earlier work this paper cites.

Active deep networks for semi-supervised sentiment classification

Zhou, Shusen, Chen, Qingcai, and Wang, Xiaolong · 2010

Earlier work this paper cites.

On the expressive power of deep architectures

Bengio, Yoshua and Delalleau, Olivier · 2011

Earlier work this paper cites.

On random weights and unsupervised feature learning

Saxe, Andrew, Koh, Pang W, Chen, Zhenghao, Bhand, Maneesh, Suresh, Bipin, and Ng, Andrew Y · 2011

Earlier work this paper cites.

Theano: new features and speed improvements

Bastien, Frédéric, Lamblin, Pascal, Pascanu, Razvan, Bergstra, James, Goodfellow, Ian J., Bergeron, Arnaud, Bouchard, Nicolas, and Bengio, Yoshua · 2012

Earlier work this paper cites.

Imagenet classification with deep convolutional neural networks

Krizhevsky, Alex, Sutskever, Ilya, and Hinton, Geoffrey E · 2012

Cited alongside, same era.

Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude

Tieleman, Tijmen and Hinton, Geoffrey · 2012

Cited alongside, same era.

Representation learning: A review and new perspectives

Bengio, Y., Courville, A., and Vincent, P · 2013

Cited alongside, same era.

Generating sequences with recurrent neural networks

Original

Graves, Alex · 2013

Cited alongside, same era.

Some improvements on deep convolutional neural network based image classification

Original

Howard, Andrew G · 2013

Cited alongside, same era.

Semi-supervised learning with deep generative models

Kingma, Diederik P, Mohamed, Shakir, Rezende, Danilo Jimenez, and Welling, Max · 2014

Later among the works it cites.

Very deep convolutional networks for large-scale image recognition

Original

Simonyan, Karen and Zisserman, Andrew · 2014

Later among the works it cites.

Dropout: A simple way to prevent neural networks from overfitting

Srivastava, Nitish, Hinton, Geoffrey, Krizhevsky, Alex, Sutskever, Ilya, and Salakhutdinov, Ruslan · 2014

Later among the works it cites.

A committee of one

Gammelsaeter, Martin · 2015

Closest in time.

Efficient batchwise dropout training using submatrices

Original

Graham, Ben, Reizenstein, Jeremy, and Robinson, Leigh · 2015

Closest in time.