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arXiv Machine Learning By Shijun Zhang, Zuowei Shen, Yuesheng Xu

Layer-wise Geometric Approximation Rates for Deep Networks

Read the original on arXiv Machine Learning →

arXiv:2604. 20219v2 Announce Type: replace Abstract: Depth is widely viewed as a central contributor to the success of deep neural networks, whereas standard neural network approximation theory typically provides guarantees only for the final output and leaves the role of intermediate layers largely unclear.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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