CSAIL Event Calendar: Previous Series 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.