Multirate Training of Neural Networks

Tiffany J Vlaar, Benedict Leimkuhler
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:22342-22360, 2022.

Abstract

We propose multirate training of neural networks: partitioning neural network parameters into "fast" and "slow" parts which are trained on different time scales, where slow parts are updated less frequently. By choosing appropriate partitionings we can obtain substantial computational speed-up for transfer learning tasks. We show for applications in vision and NLP that we can fine-tune deep neural networks in almost half the time, without reducing the generalization performance of the resulting models. We analyze the convergence properties of our multirate scheme and draw a comparison with vanilla SGD. We also discuss splitting choices for the neural network parameters which could enhance generalization performance when neural networks are trained from scratch. A multirate approach can be used to learn different features present in the data and as a form of regularization. Our paper unlocks the potential of using multirate techniques for neural network training and provides several starting points for future work in this area.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-vlaar22b, title = {Multirate Training of Neural Networks}, author = {Vlaar, Tiffany J and Leimkuhler, Benedict}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {22342--22360}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/vlaar22b/vlaar22b.pdf}, url = {https://proceedings.mlr.press/v162/vlaar22b.html}, abstract = {We propose multirate training of neural networks: partitioning neural network parameters into "fast" and "slow" parts which are trained on different time scales, where slow parts are updated less frequently. By choosing appropriate partitionings we can obtain substantial computational speed-up for transfer learning tasks. We show for applications in vision and NLP that we can fine-tune deep neural networks in almost half the time, without reducing the generalization performance of the resulting models. We analyze the convergence properties of our multirate scheme and draw a comparison with vanilla SGD. We also discuss splitting choices for the neural network parameters which could enhance generalization performance when neural networks are trained from scratch. A multirate approach can be used to learn different features present in the data and as a form of regularization. Our paper unlocks the potential of using multirate techniques for neural network training and provides several starting points for future work in this area.} }
Endnote
%0 Conference Paper %T Multirate Training of Neural Networks %A Tiffany J Vlaar %A Benedict Leimkuhler %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-vlaar22b %I PMLR %P 22342--22360 %U https://proceedings.mlr.press/v162/vlaar22b.html %V 162 %X We propose multirate training of neural networks: partitioning neural network parameters into "fast" and "slow" parts which are trained on different time scales, where slow parts are updated less frequently. By choosing appropriate partitionings we can obtain substantial computational speed-up for transfer learning tasks. We show for applications in vision and NLP that we can fine-tune deep neural networks in almost half the time, without reducing the generalization performance of the resulting models. We analyze the convergence properties of our multirate scheme and draw a comparison with vanilla SGD. We also discuss splitting choices for the neural network parameters which could enhance generalization performance when neural networks are trained from scratch. A multirate approach can be used to learn different features present in the data and as a form of regularization. Our paper unlocks the potential of using multirate techniques for neural network training and provides several starting points for future work in this area.
APA
Vlaar, T.J. & Leimkuhler, B.. (2022). Multirate Training of Neural Networks. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:22342-22360 Available from https://proceedings.mlr.press/v162/vlaar22b.html.

Related Material