The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training

Dumitru Erhan, Pierre-Antoine Manzagol, Yoshua Bengio, Samy Bengio, Pascal Vincent
; Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, PMLR 5:153-160, 2009.

Abstract

Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a kind of regularizer. We show the influence of architecture depth, model capacity, and number of training examples.

Cite this Paper


BibTeX
@InProceedings{pmlr-v5-erhan09a, title = {The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training}, author = {Dumitru Erhan and Pierre-Antoine Manzagol and Yoshua Bengio and Samy Bengio and Pascal Vincent}, booktitle = {Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics}, pages = {153--160}, year = {2009}, editor = {David van Dyk and Max Welling}, volume = {5}, series = {Proceedings of Machine Learning Research}, address = {Hilton Clearwater Beach Resort, Clearwater Beach, Florida USA}, month = {16--18 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v5/erhan09a/erhan09a.pdf}, url = {http://proceedings.mlr.press/v5/erhan09a.html}, abstract = {Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a kind of regularizer. We show the influence of architecture depth, model capacity, and number of training examples.} }
Endnote
%0 Conference Paper %T The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training %A Dumitru Erhan %A Pierre-Antoine Manzagol %A Yoshua Bengio %A Samy Bengio %A Pascal Vincent %B Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2009 %E David van Dyk %E Max Welling %F pmlr-v5-erhan09a %I PMLR %J Proceedings of Machine Learning Research %P 153--160 %U http://proceedings.mlr.press %V 5 %W PMLR %X Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a kind of regularizer. We show the influence of architecture depth, model capacity, and number of training examples.
RIS
TY - CPAPER TI - The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training AU - Dumitru Erhan AU - Pierre-Antoine Manzagol AU - Yoshua Bengio AU - Samy Bengio AU - Pascal Vincent BT - Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics PY - 2009/04/15 DA - 2009/04/15 ED - David van Dyk ED - Max Welling ID - pmlr-v5-erhan09a PB - PMLR SP - 153 DP - PMLR EP - 160 L1 - http://proceedings.mlr.press/v5/erhan09a/erhan09a.pdf UR - http://proceedings.mlr.press/v5/erhan09a.html AB - Whereas theoretical work suggests that deep architectures might be more efficient at representing highly-varying functions, training deep architectures was unsuccessful until the recent advent of algorithms based on unsupervised pretraining. Even though these new algorithms have enabled training deep models, many questions remain as to the nature of this difficult learning problem. Answering these questions is important if learning in deep architectures is to be further improved. We attempt to shed some light on these questions through extensive simulations. The experiments confirm and clarify the advantage of unsupervised pre-training. They demonstrate the robustness of the training procedure with respect to the random initialization, the positive effect of pre-training in terms of optimization and its role as a kind of regularizer. We show the influence of architecture depth, model capacity, and number of training examples. ER -
APA
Erhan, D., Manzagol, P., Bengio, Y., Bengio, S. & Vincent, P.. (2009). The Difficulty of Training Deep Architectures and the Effect of Unsupervised Pre-Training. Proceedings of the Twelth International Conference on Artificial Intelligence and Statistics, in PMLR 5:153-160

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