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A Product of Experts Approach to Early-Exit Ensembles
James Allingham · Eric Nalisnick

Fri Jul 22 12:15 PM -- 01:15 PM (PDT) @
in Dynamic Neural Networks »

Ensembles are often expensive to evaluate since they require running multiple models—each of which is costly in the case of neural networks. Using ensembles in compute-constrained applications would be much more practical if just a subset of the models could be evaluated. We address this issue with a novel product-of-experts-based method for early-exit ensembling. We rely on the fact that the product of finite-support probability distributions (e.g., the continuous uniform) has support less than or equal to that of the multiplicands. Thus, by setting a confidence threshold, we can stop evaluating ensemble members once the size of the support has been sufficiently reduced. We demonstrate our methodology for both real-value regression and multi-class classification.

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Author Information

James Allingham (University of Cambridge)
Eric Nalisnick (University of Amsterdam)

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