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Optimal Bounds between f-Divergences and Integral Probability Metrics
The families of f-divergences (e. g. the Kullback-Leibler divergence) and Integral Probability Metrics (e. g. total variation distance or maximum mean discrepancies) are commonly used in optimization and estimation.
Optimal Bounds between $f$-Divergences and Integral Probability Metrics
The families of $f$-divergences (e. g. the Kullback-Leibler divergence) and Integral Probability Metrics (e. g. total variation distance or maximum mean discrepancies) are widely used to quantify the similarity between probability distributions.
Morph: Flexible Acceleration for 3D CNN-based Video Understanding
Morph further achieves a 15. 9x average energy reduction on 3D CNNs when compared to Eyeriss.
UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition
This paper studies how weight repetition ---when the same weight occurs multiple times in or across weight vectors--- can be exploited to save energy and improve performance during CNN inference.
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