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Bounds on performance (MLR-PERF)

An Efficient Alternative to Network Pruning through Ensemble Learning


Convolutional Neural Networks (CNNs) currently represent the best tool for classification of image content. CNNs are trained in order to develop generalized expressions in form of unique features to distinguish different classes. During this process, one or more filter weights might develop the same or similar values. In this case, the redundant filters can be pruned without damaging accuracy.Unlike normal pruning methods, we investigate the possibility of replacing a full-sized convolutional neural network with an ensemble of its narrow versions.

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Authors:
Martin Poellot, Rui Zhang, André Kaup
Submitted On:
29 May 2020 - 8:29am
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[1] Martin Poellot, Rui Zhang, André Kaup, "An Efficient Alternative to Network Pruning through Ensemble Learning", IEEE SigPort, 2020. [Online]. Available: http://sigport.org/5447. Accessed: Oct. 01, 2020.
@article{5447-20,
url = {http://sigport.org/5447},
author = {Martin Poellot; Rui Zhang; André Kaup },
publisher = {IEEE SigPort},
title = {An Efficient Alternative to Network Pruning through Ensemble Learning},
year = {2020} }
TY - EJOUR
T1 - An Efficient Alternative to Network Pruning through Ensemble Learning
AU - Martin Poellot; Rui Zhang; André Kaup
PY - 2020
PB - IEEE SigPort
UR - http://sigport.org/5447
ER -
Martin Poellot, Rui Zhang, André Kaup. (2020). An Efficient Alternative to Network Pruning through Ensemble Learning. IEEE SigPort. http://sigport.org/5447
Martin Poellot, Rui Zhang, André Kaup, 2020. An Efficient Alternative to Network Pruning through Ensemble Learning. Available at: http://sigport.org/5447.
Martin Poellot, Rui Zhang, André Kaup. (2020). "An Efficient Alternative to Network Pruning through Ensemble Learning." Web.
1. Martin Poellot, Rui Zhang, André Kaup. An Efficient Alternative to Network Pruning through Ensemble Learning [Internet]. IEEE SigPort; 2020. Available from : http://sigport.org/5447

Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)


In this paper, we derive generic bounds on the maximum deviations in prediction errors for sequential prediction via an information-theoretic approach. The fundamental bounds are shown to depend only on the conditional entropy of the data point to be predicted given the previous data points. In the asymptotic case, the bounds are achieved if and only if the prediction error is white and uniformly distributed.

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Authors:
Song Fang, Quanyan Zhu
Submitted On:
24 October 2019 - 4:45pm
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[1] Song Fang, Quanyan Zhu, "Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)", IEEE SigPort, 2019. [Online]. Available: http://sigport.org/4890. Accessed: Oct. 01, 2020.
@article{4890-19,
url = {http://sigport.org/4890},
author = {Song Fang; Quanyan Zhu },
publisher = {IEEE SigPort},
title = {Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)},
year = {2019} }
TY - EJOUR
T1 - Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)
AU - Song Fang; Quanyan Zhu
PY - 2019
PB - IEEE SigPort
UR - http://sigport.org/4890
ER -
Song Fang, Quanyan Zhu. (2019). Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization). IEEE SigPort. http://sigport.org/4890
Song Fang, Quanyan Zhu, 2019. Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization). Available at: http://sigport.org/4890.
Song Fang, Quanyan Zhu. (2019). "Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization)." Web.
1. Song Fang, Quanyan Zhu. Generic Bounds on the Maximum Deviations in Sequential/Sequence Prediction (and the Implications in Recursive Algorithms and Learning/Generalization) [Internet]. IEEE SigPort; 2019. Available from : http://sigport.org/4890

EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST

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Authors:
Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney
Submitted On:
13 April 2018 - 9:55pm
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[1] Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney, "EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2783. Accessed: Oct. 01, 2020.
@article{2783-18,
url = {http://sigport.org/2783},
author = {Tiexing Wang; Donald J. Bucci Jr.; Yingbin Liang; Biao Chen; Pramod K Varshney },
publisher = {IEEE SigPort},
title = {EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST},
year = {2018} }
TY - EJOUR
T1 - EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST
AU - Tiexing Wang; Donald J. Bucci Jr.; Yingbin Liang; Biao Chen; Pramod K Varshney
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2783
ER -
Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney. (2018). EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST. IEEE SigPort. http://sigport.org/2783
Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney, 2018. EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST. Available at: http://sigport.org/2783.
Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney. (2018). "EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST." Web.
1. Tiexing Wang, Donald J. Bucci Jr., Yingbin Liang, Biao Chen, Pramod K Varshney. EXPONENTIALLY CONSISTENT K-MEANS CLUSTERING ALGORITHM BASED ON KOLMOGROV-SMIRNOV TEST [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2783