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Learning From Snapshot Examples
Examples are a powerful tool for teaching both humans and computers. In order to learn from examples, however, a student must first extract the examples from its stream of perception. Snapshot learning is a general approach to this problem, in which relevant samples of perception are used as examples. Learning from these examples can in turn improve the judgement of the snapshot mechanism, improving the quality of future examples. One way to implement snapshot learning is the Top-Cliff heuristic, which identifies relevant samples using a generalized notion of peaks. I apply snapshot learning with the Top-Cliff heuristic to solve a distributed learning problem and show that the resulting system learns rapidly and robustly, and can hallucinate useful examples in a perceptual stream from a teacherless system.