linearization
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PathSample-AnalyticGradientEstimators forStochasticBinaryNetworks
We experimentally show higher accuracy in gradient estimation and demonstrate a more stable and better performing training in deep convolutional modelswithbothproposedmethods.
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Probabilistic inverse optimal control for non-linear partially observable systems disentangles perceptual uncertainty and behavioral costs
Inverse optimal control can be used to characterize behavior in sequential decision-making tasks.
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BackpropagatingLinearlyImprovesTransferability ofAdversarialExamples
While highly efficient, themethod exploits only a coarse approximation to the loss landscape and can easily fail when a small value is required. Aiming at more powerful attacks, I-FGSM [28] and PGD [32] are further introduced to generate adversarial examples inaniterativemanner.
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