Independent Gaussian Distributions Minimize the Kullback-Leibler (KL) Divergence from Independent Gaussian Distributions
This short note is on a property of the Kullback-Leibler (KL) divergence which indicates that independent Gaussian distributions minimize the KL divergence from given independent Gaussian distributions. The primary purpose of this note is for the referencing of papers that need to make use of this property entirely or partially.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization
To achieve scalable and accurate inference for latent Gaussian processes, we propose a variational approximation based on a family of Gaussian distributions whose covariance matrices have sparse inverse Cholesky (SIC) fa…
Gaussian ProcessesVector Quantization by Minimizing Kullback-Leibler Divergence
This paper proposes a new method for vector quantization by minimizing the Kullback-Leibler Divergence between the class label distributions over the quantization inputs, which are original vectors, and the output, which…
General Classificationimage-classificationImage ClassificationQuantizationLearning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence
Learning word representations has garnered greater attention in the recent past due to its diverse text applications. Word embeddings encapsulate the syntactic and semantic regularities of sentences. Modelling word embed…
Word EmbeddingsWord SimilarityOn the Properties of Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
Kullback-Leibler (KL) divergence is one of the most important divergence measures between probability distributions. In this paper, we prove several properties of KL divergence between multivariate Gaussian distributions…
Anomaly DetectionSafe Reinforcement LearningRelaxed Triangle Inequality for Kullback-Leibler Divergence Between Multivariate Gaussian Distributions
The Kullback-Leibler (KL) divergence is not a proper distance metric and does not satisfy the triangle inequality, posing theoretical challenges in certain practical applications. Existing work has demonstrated that KL d…
Out-of-Distribution DetectionReinforcement Learning