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Towards a Universal Gating Network for Mixtures of Experts
[article]
2020
arXiv
pre-print
The combination and aggregation of knowledge from multiple neural networks can be commonly seen in the form of mixtures of experts. However, such combinations are usually done using networks trained on the same tasks, with little mention of the combination of heterogeneous pre-trained networks, especially in the data-free regime. This paper proposes multiple data-free methods for the combination of heterogeneous neural networks, ranging from the utilization of simple output logit statistics, to
arXiv:2011.01613v1
fatcat:4pbgi74ghreobm7xlhrsnlq5dm