The Kullback–Leibler Divergence Between Lattice Gaussian Distributions [PDF]
Journal of the Indian Institute of Science, 2022 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire +2 more sources
A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long‐Read Data
Advanced Science, EarlyView.SpecImmune introduces a unified computational framework optimized for long‐read sequencing to resolve over 400 highly polymorphic immune genes. This scalable approach achieves high‐resolution typing, enabling the discovery of cross‐family co‐evolutionary networks and population‐specific diversity.
Shuai Wang +5 more
wiley +1 more source
Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks
Entropy, 2020 Generative Adversarial Nets (GANs) are one of the most popular architectures for image generation, which has achieved significant progress in generating high-resolution, diverse image samples. The normal GANs are supposed to minimize the Kullback–Leibler
Likun Cai +4 more
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Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring
Advanced Science, EarlyView.This study presents a semantic representation framework for clinically interpretable cardiac monitoring from contactless radio signals. It formulates radio semantic learning as an information‐bottleneck problem and approximates the objective via intra‐modal compression and cross‐modal alignment, structuring radio measurements into meaningful semantic ...
Jinbo Chen +10 more
wiley +1 more source
Distributed Vector Quantization Based on Kullback-Leibler Divergence
Entropy, 2015 The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data.
Pengcheng Shen +2 more
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On the symmetrized S-divergence [PDF]
ITM Web of Conferences, 2019 In this paper we worked with the relative divergence of type s, s ∈ ℝ, which include Kullback-Leibler divergence and the Hellinger and χ2 distances as particular cases.
Simić Slavko
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On the Use of Kullback–Leibler Divergence for Kernel Selection and Interpretation in Variational Autoencoders for Feature Creation [PDF]
, 2023 Fábio Mendonça +3 more
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PAIR: Reconstructing Single‐Cell Open‐Chromatin Landscapes for Transcription Factor Regulome Mapping
Advanced Science, EarlyView.scATAC‐seq analysis is often constrained by limited sequencing depth, extreme sparsity, and pervasive technical missingness. PAIR is a probabilistic framework that restores scATAC‐seq accessibility profiles by directly modeling the native cell–peak bipartite structure of chromatin accessibility.
Yanchi Su +7 more
wiley +1 more source
Advanced Intelligent Discovery, EarlyView.Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley +1 more source
f-divergence Analysis of Generative Adversarial Network
Foundations of Computing and Decision SciencesWe aim to establish estimation bounds for various divergences, including total variation, Kullback-Leibler (KL) divergence, Hellinger divergence, and Pearson χ2 divergence, within the GAN estimator.
Hasan Mahmud, Sang Hailin
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