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Award ID contains: 1619452

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  1. ; (, IEEE)
    Given a length n sample from R^d and a neural network with a fixed architecture with W weights, k neurons, linear threshold activation functions, and binary outputs on each neuron, we study the problem of uniformly sampling from all possible labelings on the sample corresponding to different choices of weights. We provide an algorithm that runs in time polynomial both in n and W such that any labeling appears with probability at least (W2ekn)^W for W < n. For a single neuron, we also provide a random walk based algorithm that samples exactly uniformly. 
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  2. ; ; (, IEEE)
    Full version with complete proofs attached. Please email NS (email in paper) for other versions if desired. 
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    Full Text Available