Complexity theory -is the study of resource-bounded computation. The aim of this project is to study the-amount of resources, in particular, time and hardware, used in neural network computations.
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Information complexity is to an extent the “second half” of complexity theory for neural networks, that which deals with information issues, and numbers of ...
Jan 26, 2020 · Neural networks can simulate complexity but aren't taking advantage of complexity. It should be possible to meld the two: a complex network of intelligent ...
One of the aims of this book is to compare the complexity of neural networks and the complexity of conventional computers, looking at the computational ability ...
Jan 16, 2022 · We propose a complexity measure of a neural network mapping function based on the order and diversity of the set of tangent spaces from different inputs.
Jul 30, 2005 · We survey some of the central results in the complexity theory of neural networks, with pointers to the literature.
Mar 18, 2021 · We define a notion of complexity, which quantifies the nonlinearity of the computation of a neural network, as well as a complementary measure ...
Using the tools of complexity theory, Stephen Judd develops a formal description of associative learning in connectionist networks.
Abstract. We survey some of the central results in the complexity theory of neural networks, with pointers to the literature. 1 Introduction.
This paper is an introduction for the non-expert to the theory of artificial neural networks as embodied in current versions of feedforward neural networks.