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1,223 claims from 771 papers are on the record. 45 have been checked so far; the other 1,178 have no check with a result yet.

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Keyword: speech recognition ×Clear all

4 claims from 3 papers

1. Computer Science › Neural Networks and Applications

   Deep learning

   LeCun, Bengio and Hinton · Nature · 2015

   This review describes deep learning, its use of backpropagation, and its reported improvements in speech, image and other tasks, via convolutional and recurrent networks.

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   1. UncheckedDeep learning lets models built from many processing layers learn representations of data at several levels of abstraction.“Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.”

2. Computer Science › Neural Networks and Applications

   Dropout: a simple way to prevent neural networks from overfitting

   Srivastava, Hinton, Krizhevsky, Sutskever and Salakhutdinov · 2014

   The paper presents dropout, which randomly drops units during training, and reports that it improves neural networks on vision, speech, text classification and computational biology tasks.

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   1. UncheckedUsing dropout at test time via a single network with smaller weights is said to cut overfitting a lot and beat other regularization methods.“This significantly reduces overfitting and gives major improvements over other regularization methods.”

3. Computer Science › Neural Networks and Applications

   Do Deep Nets Really Need to be Deep?

   Ba and Caruana · arXiv (Cornell University) · 2013

   The extended abstract reports that shallow networks trained to mimic deep models can match their accuracy on TIMIT phoneme recognition, sometimes with similar parameter counts.

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   1. UncheckedShallow feed-forward networks can learn functions previously learned by deep nets and reach accuracies once thought to need deep models.“In this extended abstract, we show that shallow feed-forward networks can learn the complex functions previously learned by deep nets and achieve accuracies previously only achievable with deep models.”
   2. UncheckedIn some cases, shallow neural networks can learn the functions of deep models using a similar total number of parameters to the original deep model.“Moreover, in some cases the shallow neural nets can learn these deep functions using a total number of parameters similar to the original deep model.”