Affine symmetries and neural network identifiability
AuthorsVerner Vlačić and Helmut Bölcskei
ReferenceAdvances in Mathematics, Vol. 376, Article 107485, pp. 1-72, Jan. 2021.
AbstractWe address the following question of neural network identifiability: Suppose we are given a function f:R^m-->R^n and a nonlinearity rho. Can we specify the architecture, weights, and biases of all feed-forward neural networks with respect to rho giving rise to f? Existing literature on the subject suggests that the answer should be yes, provided we are only concerned with finding networks that satisfy certain "genericity conditions''. Moreover, the identified networks are mutually related by symmetries of the nonlinearity. For instance, the tanh function is odd, and so flipping the signs of the incoming and outgoing weights of a neuron does not change the output map of the network. The results known hitherto, however, apply either to single-layer networks, or to networks satisfying specific structural assumptions (such as full connectivity), as well as to specific nonlinearities. In an effort to answer the identifiability question in greater generality, we consider arbitrary nonlinearities with potentially complicated affine symmetries, and we show that the symmetries can be used to find a rich set of networks giving rise to the same function f. The set obtained in this manner is, in fact, exhaustive (i.e., it contains all networks giving rise to f) unless there exists a network A "with no internal symmetries'' giving rise to the identically zero function. This result can thus be interpreted as an analog of the rank-nullity theorem for linear operators. We furthermore exhibit a class of "tanh-type" nonlinearities (including the tanh function itself) for which such a network A does not exist, thereby solving the identifiability question for these nonlinearities in full generality and settling an open problem posed by Fefferman in . Finally, we show that this class contains nonlinearities with arbitrarily complicated symmetries.
KeywordsNeural networks, identifiability, symmetries, analytic continuation
Download this document:
Copyright Notice: © 2021 V. Vlačić and H. Bölcskei.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.