Feb. 12, 2024, 5:43 a.m. | Rupert Mitchell Robin Menzenbach Kristian Kersting Martin Mundt

cs.LG updates on arXiv.org arxiv.org

The results of training a neural network are heavily dependent on the architecture chosen; and even a modification of only its size, however small, typically involves restarting the training process. In contrast to this, we begin training with a small architecture, only increase its capacity as necessary for the problem, and avoid interfering with previous optimization while doing so. We thereby introduce a natural gradient based approach which intuitively expands both the width and depth of a neural network when …

architecture capacity contrast cs.lg network networks neural network neural networks optimization process small training

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