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Yes, my question was more about meta-level algorithms for balancing size against performance. Especially adaptive methods such that we're not just growing up to
by chriswarbo 10y ago
Yes, my question was more about meta-level algorithms for balancing size against performance. Especially adaptive methods such that we're not just growing up to a limit and stopping, but selectively allocating resources to those parts which need them. Adapting over time would be nice too: "thinking harder" when there are idle resources, but shrinking the results back down under load.
- SimonKinds 10y agoThis paper http://dl.acm.org/citation.cfm?id=2830854 http://dl.acm.org/citation.cfm?id=2830854 kind of has a solution to being more efficient. It has two networks and uses the smaller one (more efficient) to infere first. If the result is accurate with high probability (the probability of one class is much larger than the probability of any other class) then there is no need to run the big (expensive) network.