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An algorithm is developed that learns where to collect information while ensuring that the required samples can be obtained efficiently, and a matching information theoretic lower bound is proved, showing that learning from a single trajectory is no harder in the worst case than learning with unrestricted access to sta...
This work shows that changing the scale of initial weights can determine which neurons learn a feature without altering any neuron's initial contribution, and reveals how initialization can control the organization of a learned representation without changing what the network initially represents.
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