Feb 9, 2015 · Performing dropout batchwise, so that one pattern of dropout is used for each sample in a minibatch, we can substantially reduce training times.

Feb 9, 2015 · Abstract. Dropout is a popular technique for regularizing artificial neural networks. Dropout networks are generally trained by minibatch ...

This work explores a very simple alternative to the dropout mask, and instead of masking dropped out units by setting them to zero, performs matrix ...

Performing dropout batchwise, so that one pattern of dropout is used for each sample in a minibatch, we can substantially reduce training times. Batchwise ...

Benjamin Graham, Jeremy Reizenstein, Leigh Robinson: Efficient batchwise dropout training using submatrices. CoRR abs/1502.02478 (2015).

Efficient batchwise dropout training using submatrices ... Dropout networks are generally trained by minibatch gradient descent with a dropout mask turning off ...

Efficient batchwise dropout training using submatrices. B Graham, J Reizenstein, L Robinson. arXiv preprint arXiv:1502.02478, 2015. 19, 2015. Areas of areas ...

Efficient batchwise dropout training using submatrices. arXiv preprint arXiv:1502.02478, 2015. [6] B. Settles. Active Learning, volume 6 of Synthesis ...

Efficient batchwise dropout training using submatrices · no code ... Dropout networks are generally trained by minibatch gradient descent with a dropout ...

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random.