Results 71 to 80 of about 11,282 (244)
Quantizations Preserving Kullback-Leibler Divergence
, 2018 International Zurich Seminar on Information and Communication (IZS 2018 ...
Huleihel, Wasim +2 more
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Advanced Intelligent Systems, EarlyView.Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee +7 more
wiley +1 more source
On the Kullback-Leibler divergence between pairwise isotropic Gaussian-Markov random fields [PDF]
, 2022 Alexandre L. M. Levada
openalex +1 more source
Decoupled Kullback-Leibler Divergence Loss
Advances in Neural Information Processing Systems 37In this paper, we delve deeper into the Kullback-Leibler (KL) Divergence loss and mathematically prove that it is equivalent to the Decoupled Kullback-Leibler (DKL) Divergence loss that consists of 1) a weighted Mean Square Error (wMSE) loss and 2) a Cross-Entropy loss incorporating soft labels. Thanks to the decomposed formulation of DKL loss, we have
Cui, Jiequan +5 more
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Model Fusion with Kullback--Leibler Divergence
, 2020 We propose a method to fuse posterior distributions learned from heterogeneous datasets. Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors and proceeds using a simple assign-and-average approach.
Claici, Sebastian +3 more
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Ellipticity and Circularity Measuring via Kullback–Leibler Divergence [PDF]
Journal of Mathematical Imaging and Vision, 2015 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Misztal, Krzysztof, Tabor, Jacek
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Advanced Intelligent Systems, EarlyView.This study presents a new sampling‐based model predictive control minimizing reverse Kullback‐Leibler divergence to quickly find a local optimum. In addition, a modified Nesterov's acceleration method is introduced for faster convergence. The method is effective for real‐time simulations and real‐world operability improvement on a force‐driven mobile ...
Taisuke Kobayashi, Kota Fukumoto
wiley +1 more source
Restricted Tweedie stochastic block models
Canadian Journal of Statistics, EarlyView.Abstract The stochastic block model (SBM) is a widely used framework for community detection in networks, where the network structure is typically represented by an adjacency matrix. However, conventional SBMs are not directly applicable to an adjacency matrix that consists of nonnegative zero‐inflated continuous edge weights.
Jie Jian, Mu Zhu, Peijun Sang
wiley +1 more source
On the symmetrized s-divergence
Open Mathematics, 2020 In this study, we work with the relative divergence of type s,s∈ℝs,s\in {\mathbb{R}}, which includes the Kullback-Leibler divergence and the Hellinger and χ 2 distances as particular cases.
Simić Slavko +2 more
doaj +1 more source
Covariance Structure Modeling of Engineering Demand Parameters in Cloud‐Based Seismic Analysis
Earthquake Engineering &Structural Dynamics, EarlyView.ABSTRACT Probabilistic seismic demand modeling aims to estimate structural demand as a function of ground motion intensity—a critical stage in seismic risk assessment. Although many models exist to describe the structural demand, few consider the covariance among engineering demand parameters, potentially overlooking a key factor in improving the ...
Archie Rudman +3 more
wiley +1 more source