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The tractability of neural-network approximation is investigated. The dependence of worst-case errors on the number of variables is studied.
Widely-used connectionistic models take on the form of linear combinations of all n-tuples of functions computable by computational units of various kinds.
The tractability of neural-network approximation is investigated and the dependence of worst-case errors on the number of variables is studied.
The tractability of neural-network approximation is investigated. The dependence of worst-case errors on the number of variables is studied.
Our results include tractability theorems for integration with respect to non-tensor product measures, and over unbounded and/or non-tensor product subsets.
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Our goal is to compile the Bool- ean function specified by a neural network into a tractable. Boolean circuit that facilitates explanation and verification. We ...
The tractability of neural-network approximation is investigated. The dependence of worst-case errors on the number of variables is studied.
Aug 7, 2023 · In this paper, we present a sharper version of the results in the paper Dimension independent bounds for general shallow networks.
In this article we obtain novel algorithmic upper bounds for training linear- and ReLU-activated neural networks to optimality which push the boundaries of ...
Within the variational frame- work for approximating these models, we present two classes of dis- tributions, decimatable Boltzmann Machines and Tractable ...