Thee KL divergence is the most commonly used measure for comparing query and document
language models in the language modeling framework to ad hoc retrieval. Since KL is …

… over the mean-seeking forward Kullback-Leibler (FKL) divergence, this study empirically
and … we can revisit the mode-seeking and mean-seeking behaviors in the view of fdivergence: …

… The reason why the balancing policy between reconstruction error and KL-regularization
addressed in [9] and revisited in this article is so effective seems to rely on its laziness in the …

… Kullback-Leibler (KL) divergence is one of the most important measures to calculate the
difference between probability distributions. In this paper, we theoretically study several …

… The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that
quantifies the proximity of two probability distributions. Although difficult to understand by …

… Generally, the objective function of KD is the Kullback-Leibler (KL) divergence loss between
the … combination of the cross-entropy loss LCE and the KullbackLeibler divergence loss LKL: …

… To do so, we use a Kullback–Leibler divergence approach to measure the nonlinearity of
the measurement, which is theoretically more sound than the nonlinearity measure used in the …

… on the quantitative results obtained with our Kullback-Leibler divergence Dτ , we now turn to
… Arneodo, Revisiting multifractality of high-resolution temporal rainfall using a wavelet-based …

… The Kullback-Leibler (KL) divergence is a fundamental equation of information theory that
quantifies the proximity of two probability distributions. Although difficult to understand by …

… et al., knowledge distillation based on KullbackLeibler Divergence (KL-Div) has been …
Revisit the power of vanilla knowledge distillation: from small scale to large scale. In A. Oh, T…