A hierarchical RNN with bidirectional minGRU scans reports higher ImageNet accuracy than DeiT backbones at lower high-resolution FLOPs.
Log-concavity and log-convexity of series containing multiple Pochhammer symbols
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abstract
In this paper, we study power series with coefficients equal to a product of a generic sequence and an explicitly given function of a positive parameter expressible in terms of the Pochhammer symbols. Four types of such series are treated. We show that logarithmic concavity (convexity) of the generic sequence leads to logarithmic concavity (convexity) of the sum of the series with respect to the argument of the explicitly given function. The logarithmic concavity (convexity) is derived from a stronger property, namely, positivity (negativity) of the power series coefficients of the so-called generalized Tur\'{a}nian. Applications to special functions such as the generalized hypergeometric function and the Fox-Wright function are also discussed.
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VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis
A hierarchical RNN with bidirectional minGRU scans reports higher ImageNet accuracy than DeiT backbones at lower high-resolution FLOPs.