Limits of End-to-End Learning

Tobias Glasmachers
Proceedings of the Ninth Asian Conference on Machine Learning, PMLR 77:17-32, 2017.

Abstract

End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning systems are specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all “peripheral” modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex. In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of \emphscaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.

Cite this Paper

BibTeX
@InProceedings{pmlr-v77-glasmachers17a, title = {Limits of End-to-End Learning}, author = {Glasmachers, Tobias}, booktitle = {Proceedings of the Ninth Asian Conference on Machine Learning}, pages = {17--32}, year = {2017}, editor = {Zhang, Min-Ling and Noh, Yung-Kyun}, volume = {77}, series = {Proceedings of Machine Learning Research}, address = {Yonsei University, Seoul, Republic of Korea}, month = {15--17 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v77/glasmachers17a/glasmachers17a.pdf}, url = {https://proceedings.mlr.press/v77/glasmachers17a.html}, abstract = {End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning systems are specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all “peripheral” modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex. In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of \emphscaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.} }
Endnote
%0 Conference Paper %T Limits of End-to-End Learning %A Tobias Glasmachers %B Proceedings of the Ninth Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Min-Ling Zhang %E Yung-Kyun Noh %F pmlr-v77-glasmachers17a %I PMLR %P 17--32 %U https://proceedings.mlr.press/v77/glasmachers17a.html %V 77 %X End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning systems are specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all “peripheral” modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex. In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of \emphscaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.
APA
Glasmachers, T.. (2017). Limits of End-to-End Learning. Proceedings of the Ninth Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 77:17-32 Available from https://proceedings.mlr.press/v77/glasmachers17a.html.

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