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Electronic Colloquium on Computational Complexity

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REPORTS > AUTHORS > OHAD SHAMIR:
All reports by Author Ohad Shamir:

TR20-085 | 5th June 2020
Gal Vardi, Ohad Shamir

Neural Networks with Small Weights and Depth-Separation Barriers

In studying the expressiveness of neural networks, an important question is whether there are functions which can only be approximated by sufficiently deep networks, assuming their size is bounded. However, for constant depths, existing results are limited to depths $2$ and $3$, and achieving results for higher depths has been ... more >>>




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