Learning the structure of generative models without labeled data
Learning the structure of generative models without labeled data Bach et al., ICML’17
For the last couple of posts we’ve been looking at Snorkel and BabbleLabble which both depend on data programming – the ability to intelligently combine the outputs of a set of labelling functions. The core of data programming is developed in two papers, ‘Data programming: creating large training sets, quickly’ (Ratner 2016) and today’s paper choice, ‘Learning the structure of generative models without labeled data’ (Bach 2017).
The original data programming paper works explicitly with input pairs (x,y) (e.g. the chemical and disease word pairs we saw from the disease task in Snorkel) which (for me at least) confuses the presentation a little compared to the latter ICML paper which just assumes inputs (which could of course have pair structure, but we don’t care about that at this level of detail). Also in the original paper dependencies between labelling functions are explicitly specified by end users (as one of four types: similar, fixing, reinforcing, and exclusive) and built into a factor graph. In the ICML paper dependencies are learned. So I’m going to work mostly from ‘Learning the structure of generative Continue reading
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