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Kernelized Synaptic Weight Matrices


Muller, Lorenz K; Martel, Julien N P; Indiveri, Giacomo (2018). Kernelized Synaptic Weight Matrices. In: International Conference on Machine Learning 2018, Stockholm, 10 July 2018 - 15 July 2018, 3651-3660.

Abstract

In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions. A layer of our network can be interpreted as introducing a (potentially infinitely wide) linear layer between input and output. We describe the theory underpinning this model and validate it with concrete examples, exploring how it can be used to impose structure on neural networks in diverse applications ranging from data visualization to recommender systems. We achieve state-of-the-art performance in a collaborative filtering task (MovieLens).

Abstract

In this paper we introduce a novel neural network architecture, in which weight matrices are re-parametrized in terms of low-dimensional vectors, interacting through kernel functions. A layer of our network can be interpreted as introducing a (potentially infinitely wide) linear layer between input and output. We describe the theory underpinning this model and validate it with concrete examples, exploring how it can be used to impose structure on neural networks in diverse applications ranging from data visualization to recommender systems. We achieve state-of-the-art performance in a collaborative filtering task (MovieLens).

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Computational Theory and Mathematics
Physical Sciences > Human-Computer Interaction
Physical Sciences > Software
Language:English
Event End Date:15 July 2018
Deposited On:12 Mar 2019 10:56
Last Modified:15 Apr 2020 23:26
Publisher:International Conference on Machine Learning
Number of Pages:10
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://proceedings.mlr.press/v80/muller18a.html

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