Deep Learning Made Easier by Linear Transformations in Perceptrons

Tapani Raiko, Harri Valpola, Yann Lecun
; Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:924-932, 2012.

Abstract

We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero activation and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. This transformation aims at separating the problems of learning the linear and nonlinear parts of the whole input-output mapping, which has many benefits. We study the theoretical properties of the transformation by noting that they make the Fisher information matrix closer to a diagonal matrix, and thus standard gradient closer to the natural gradient. We experimentally confirm the usefulness of the transformations by noting that they make basic stochastic gradient learning competitive with state-of-the-art learning algorithms in speed, and that they seem also to help find solutions that generalize better. The experiments include both classification of small images and learning a low-dimensional representation for images by using a deep unsupervised auto-encoder network. The transformations were beneficial in all cases, with and without regularization and with networks from two to five hidden layers.

Cite this Paper


BibTeX
@InProceedings{pmlr-v22-raiko12, title = {Deep Learning Made Easier by Linear Transformations in Perceptrons}, author = {Tapani Raiko and Harri Valpola and Yann Lecun}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {924--932}, year = {2012}, editor = {Neil D. Lawrence and Mark Girolami}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/raiko12/raiko12.pdf}, url = {http://proceedings.mlr.press/v22/raiko12.html}, abstract = {We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero activation and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. This transformation aims at separating the problems of learning the linear and nonlinear parts of the whole input-output mapping, which has many benefits. We study the theoretical properties of the transformation by noting that they make the Fisher information matrix closer to a diagonal matrix, and thus standard gradient closer to the natural gradient. We experimentally confirm the usefulness of the transformations by noting that they make basic stochastic gradient learning competitive with state-of-the-art learning algorithms in speed, and that they seem also to help find solutions that generalize better. The experiments include both classification of small images and learning a low-dimensional representation for images by using a deep unsupervised auto-encoder network. The transformations were beneficial in all cases, with and without regularization and with networks from two to five hidden layers.} }
Endnote
%0 Conference Paper %T Deep Learning Made Easier by Linear Transformations in Perceptrons %A Tapani Raiko %A Harri Valpola %A Yann Lecun %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-raiko12 %I PMLR %J Proceedings of Machine Learning Research %P 924--932 %U http://proceedings.mlr.press %V 22 %W PMLR %X We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero activation and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. This transformation aims at separating the problems of learning the linear and nonlinear parts of the whole input-output mapping, which has many benefits. We study the theoretical properties of the transformation by noting that they make the Fisher information matrix closer to a diagonal matrix, and thus standard gradient closer to the natural gradient. We experimentally confirm the usefulness of the transformations by noting that they make basic stochastic gradient learning competitive with state-of-the-art learning algorithms in speed, and that they seem also to help find solutions that generalize better. The experiments include both classification of small images and learning a low-dimensional representation for images by using a deep unsupervised auto-encoder network. The transformations were beneficial in all cases, with and without regularization and with networks from two to five hidden layers.
RIS
TY - CPAPER TI - Deep Learning Made Easier by Linear Transformations in Perceptrons AU - Tapani Raiko AU - Harri Valpola AU - Yann Lecun BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics PY - 2012/03/21 DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-raiko12 PB - PMLR SP - 924 DP - PMLR EP - 932 L1 - http://proceedings.mlr.press/v22/raiko12/raiko12.pdf UR - http://proceedings.mlr.press/v22/raiko12.html AB - We transform the outputs of each hidden neuron in a multi-layer perceptron network to have zero activation and zero slope on average, and use separate shortcut connections to model the linear dependencies instead. This transformation aims at separating the problems of learning the linear and nonlinear parts of the whole input-output mapping, which has many benefits. We study the theoretical properties of the transformation by noting that they make the Fisher information matrix closer to a diagonal matrix, and thus standard gradient closer to the natural gradient. We experimentally confirm the usefulness of the transformations by noting that they make basic stochastic gradient learning competitive with state-of-the-art learning algorithms in speed, and that they seem also to help find solutions that generalize better. The experiments include both classification of small images and learning a low-dimensional representation for images by using a deep unsupervised auto-encoder network. The transformations were beneficial in all cases, with and without regularization and with networks from two to five hidden layers. ER -
APA
Raiko, T., Valpola, H. & Lecun, Y.. (2012). Deep Learning Made Easier by Linear Transformations in Perceptrons. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in PMLR 22:924-932

Related Material