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- W2772087644 abstract "We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs. A prior on the latent variables expresses the preference for faster computation. The amount of computation for an input is determined via amortized maximum a posteriori (MAP) inference. MAP inference is performed using a novel stochastic variational optimization method. The recently proposed Adaptive Computation Time mechanism can be seen as an ad-hoc relaxation of this model. We demonstrate training using the general-purpose Concrete relaxation of discrete variables. Evaluation on ResNet shows that our method matches the speed-accuracy trade-off of Adaptive Computation Time, while allowing for evaluation with a simple deterministic procedure that has a lower memory footprint." @default.
- W2772087644 created "2017-12-22" @default.
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- W2772087644 date "2017-12-01" @default.
- W2772087644 modified "2023-09-28" @default.
- W2772087644 title "Probabilistic Adaptive Computation Time" @default.
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