Results 61 to 70 of about 26,245 (203)
Conversations from the Wonder Chamber: Jesse Adams Stein in conversation with Matthew Connell
, 2011 Essay in the catalogue accompanying the exhibition"Awfully Wonderful: Science Fiction in Contemporary Art" - curated by Dr Lizzie Muller & Bec Dean.
Stein, JA
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Density Property of Stein Manifolds and Holomorphic Matrix Factorization [PDF]
We present results on the density property of Stein manifolds and on factorization of holomorphic matrices. A Stein manifold with the density property has an infinite dimensional group of holomorphic automorphisms.
Huang, Gaofeng
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On the Nullstellensätze for Stein spaces and 𝐶-analytic sets
Transactions of the American Mathematical Society, 2015 In this work we prove the real Nullstellensatz for the ring O ( X
Acquistapace, Francesca +2 more
openaire +1 more source
A simple proof of Fefferman-Stein type characterization of ${\rm CMO}(\mathbb {R}^{n})$ space
summary:We give a simple proof of Fefferman-Stein type characterization of the space ${\rm CMO}(\mathbb {R}^{n})$, that is, $f\in {\rm CMO} (\mathbb {R}^{n})$ if and only if $$ f=\phi +\sum _{j=1}^{n}R_{j}\varphi _{j}, $$ where $\phi ,\varphi _{j}\in {C_{
Guo, Qingdong, Linli, Zeqiang, Hu, Kang
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An interpolation property of locally Stein sets
, 2021 We prove that, if D is a normal open subset of a Stein space X of puredimension such that D is locally Stein at every point of ∂D n Xsg, then, for every holomorphic vector bundle E over D and every discrete subset Ʌ of D \ Xsg whose set of accumulation ...
Vâjâitu, Viorel
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A Stein variational Newton method
, 2019 Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation
Spantini, Alessio, Marzouk, Youssef M
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Bergman-Einstein metric on a Stein space with a strongly pseudoconvex boundary
, 2020 Let $\Omega$ be a Stein space with a compact smooth strongly pseudoconvex boundary. We prove that the boundary is spherical if its Bergman metric over $\hbox{Reg}(\Omega)$ is K\"ahler-Einstein.Comment: 21 pages, comments are ...
Li, Xiaoshan, Huang, Xiaojun
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De-randomizing MCMC dynamics with the diffusion Stein operator [PDF]
, 2021 Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions.
Kaski, Samuel +2 more
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From KL Divergence to Wasserstein Distance: Enhancing Autoencoders with FID Analysis
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceVariational Autoencoders (VAEs) are popular Bayesian inference models that excel at approximating complex data distributions in a lower-dimensional latent space.
Laxmi Kanta Poudel +3 more
doaj +1 more source
Revista de Saúde Pública, 2007 OBJETIVO: Propor técnicas de correção de sub-registro e redistribuição de causas mal definidas para o Sistema de Informações sobre Mortalidade e o Sistema de Informações Hospitalares do SUS.
Luciana Tricai Cavalini +1 more
doaj +1 more source