This paper investigates the application of recent theoretical progress in sparse optimization to the problem of learning sparse neural networks. The key focus is on the Iterative Hard Thresholding (IHT) algorithm, a technique that can efficiently identify and learn the locations of nonzero parameters in a neural network.
The paper starts by analyzing the theoretical assumptions underlying the convergence properties of the IHT algorithm, as established in prior work. It then examines how these assumptions can be applied in the context of neural networks. Specifically, the authors address four main questions:
The authors use a single-layer neural network trained on the IRIS dataset as a testbed to validate the theoretical findings. They demonstrate that the necessary conditions for the convergence of the IHT algorithm can be reliably ensured during the training of the neural network. Under these conditions, the IHT algorithm is shown to consistently converge to a sparse local minimizer, providing empirical support for the theoretical framework.
The paper highlights the importance of understanding the theoretical foundations of sparse optimization techniques, such as IHT, in the context of simplifying complex neural network models. By establishing the applicability of these theoretical results to neural network training, the authors lay the groundwork for further exploration of sparse neural network optimization.
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by Saeed Damadi... klo arxiv.org 04-30-2024
https://arxiv.org/pdf/2404.18414.pdfSyvällisempiä Kysymyksiä