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Efficiency Evaluation of Character-level RNN Training Schedules

Cedric De Boom UGent, Sam Leroux UGent, Steven Bohez UGent, Pieter Simoens UGent, Thomas Demeester UGent and Bart Dhoedt UGent (2016) Data Efficient Machine Learning workshop, ICML.
abstract
We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectiveness as a function of training time and amount of training data seen. We conclude that the sequence to sequence training, together with sequence to sample prediction, performs the most efficient and consistent across multiple parameter settings. We show that the choice of training and prediction schedule potentially has a considerable impact on the prediction effectiveness for a given training budget.
Please use this url to cite or link to this publication:
author
organization
year
type
conference
publication status
published
subject
keyword
IBCN, neural networks, deep learning, efficiency, RNN, machine learning, text, characters
in
Data Efficient Machine Learning workshop, ICML
pages
2 pages
conference name
Data Efficient Machine Learning workshop (ICML 2016)
conference location
New York, USA
conference start
2016-06-19
conference end
2016-06-24
language
English
UGent publication?
yes
classification
C1
copyright statement
I have retained and own the full copyright for this publication
id
8023862
handle
http://hdl.handle.net/1854/LU-8023862
date created
2016-07-03 01:34:45
date last changed
2017-03-02 13:15:55
@inproceedings{8023862,
  abstract     = {We present four training and prediction schedules from the same character-level recurrent neural network. The efficiency of these schedules is tested in terms of model effectiveness as a function of training time and amount of training data seen. We conclude that the sequence to sequence training, together with sequence to sample prediction, performs the most efficient and consistent across multiple parameter settings. We show that the choice of training and prediction schedule potentially has a considerable impact on the prediction effectiveness for a given training budget.},
  author       = {De Boom, Cedric and Leroux, Sam and Bohez, Steven and Simoens, Pieter and Demeester, Thomas and Dhoedt, Bart},
  booktitle    = {Data Efficient Machine Learning workshop, ICML},
  keyword      = {IBCN,neural networks,deep learning,efficiency,RNN,machine learning,text,characters},
  language     = {eng},
  location     = {New York, USA},
  pages        = {2},
  title        = {Efficiency Evaluation of Character-level RNN Training Schedules},
  year         = {2016},
}

Chicago
De Boom, Cedric, Sam Leroux, Steven Bohez, Pieter Simoens, Thomas Demeester, and Bart Dhoedt. 2016. “Efficiency Evaluation of Character-level RNN Training Schedules.” In Data Efficient Machine Learning Workshop, ICML.
APA
De Boom, C., Leroux, S., Bohez, S., Simoens, P., Demeester, T., & Dhoedt, B. (2016). Efficiency Evaluation of Character-level RNN Training Schedules. Data Efficient Machine Learning workshop, ICML. Presented at the Data Efficient Machine Learning workshop (ICML 2016).
Vancouver
1.
De Boom C, Leroux S, Bohez S, Simoens P, Demeester T, Dhoedt B. Efficiency Evaluation of Character-level RNN Training Schedules. Data Efficient Machine Learning workshop, ICML. 2016.
MLA
De Boom, Cedric, Sam Leroux, Steven Bohez, et al. “Efficiency Evaluation of Character-level RNN Training Schedules.” Data Efficient Machine Learning Workshop, ICML. 2016. Print.