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arXiv · 1712.01897

Online Learning with Gated Linear Networks

Joel Veness·Tor Lattimore·Avishkar Bhoopchand·Agnieszka Grabska-Barwinska·Christopher Mattern·Peter Toth
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Abstract

This paper describes a family of probabilistic architectures designed for online learning under the logarithmic loss. Rather than relying on non-linear transfer functions, our method gains representational power by the use of data conditioning. We state under general conditions a learnable capacity theorem that shows this approach can in principle learn any bounded Borel-measurable function on a compact subset of euclidean space; the result is stronger than many universality results for connectionist architectures because we provide both the model and the learning procedure for which convergence is guaranteed.

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Joel Veness, Tor Lattimore, Avishkar Bhoopchand, Agnieszka Grabska-Barwinska, Christopher Mattern, Peter Toth. 2017-12-05. Online Learning with Gated Linear Networks. https://arxiv.org/abs/1712.01897

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