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Fine-tuning Handwriting Recognition systems with Temporal Dropout

Fine-tuning Handwriting Recognition systems with Temporal Dropout

Edgard Chammas
C. Mokbel
ArXivPDFHTML

Papers citing "Fine-tuning Handwriting Recognition systems with Temporal Dropout"

5 / 5 papers shown
Title
Deep Networks with Stochastic Depth
Deep Networks with Stochastic Depth
Gao Huang
Yu Sun
Zhuang Liu
Daniel Sedra
Kilian Q. Weinberger
154
2,344
0
30 Mar 2016
Recurrent Dropout without Memory Loss
Recurrent Dropout without Memory Loss
Stanislau Semeniuta
Aliaksei Severyn
Erhardt Barth
55
222
0
16 Mar 2016
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding
EESEN: End-to-End Speech Recognition using Deep RNN Models and WFST-based Decoding
Yajie Miao
M. Gowayyed
Florian Metze
81
753
0
29 Jul 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
203
18,534
0
06 Feb 2015
Improving neural networks by preventing co-adaptation of feature detectors
Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton
Nitish Srivastava
A. Krizhevsky
Ilya Sutskever
Ruslan Salakhutdinov
VLM
385
7,650
0
03 Jul 2012

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