Backpropagation-Free Deep Learning with Recursive Local Representation Alignment

Alexander G. Ororbia, Ankur Mali, Daniel Kifer, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals. Backpropagation (backprop), the workhorse for training these networks, is an inherently sequential process that is difficult to parallelize. Furthermore, researchers must continually develop various specialized techniques, such as particular weight initializations and enhanced activation functions, to ensure stable parameter optimization. Our goal is to seek an effective, neuro-biologically plausible alternative to backprop that can be used to train deep networks. In this paper, we propose a backprop-free procedure, recursive local representation alignment, for training large-scale architectures. Experiments with residual networks on CIFAR-10 and the large benchmark, ImageNet, show that our algorithm generalizes as well as backprop while converging sooner due to weight updates that are parallelizable and computationally less demanding. This is empirical evidence that a backprop-free algorithm can scale up to larger datasets.

Original languageEnglish (US)
Title of host publicationAAAI-23 Technical Tracks 8
EditorsBrian Williams, Yiling Chen, Jennifer Neville
PublisherAAAI press
Number of pages9
ISBN (Electronic)9781577358800
StatePublished - Jun 27 2023
Event37th AAAI Conference on Artificial Intelligence, AAAI 2023 - Washington, United States
Duration: Feb 7 2023Feb 14 2023

Publication series

NameProceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023


Conference37th AAAI Conference on Artificial Intelligence, AAAI 2023
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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