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An Alternative to Backpropagation

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For decades, backpropagation has been the backbone of deep learning. However, it is biologically implausible, computationally demanding, and sometimes inefficient. This has spurred the search for alternative training paradigms. I will introduce a novel gradient‑free approach in which the objective is to align the distribution of a network’s linear regions with that of the training data. On CIFAR ‑10, this approach surpasses the performance achieved by Adam, which demonstrates that carefully structuring a model’s linear regimes can provide a powerful alternative to traditional gradient‑based methods.

This talk is part of the CBL Research Talks series.

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