Results 141 to 150 of about 397 (184)

Opportunities and challenges of diffusion models for generative AI. [PDF]

Natl Sci Rev
Chen M, Mei S, Fan J, Wang M.
europepmc   +1 more source

Lower Bounds on the Noiseless Worst-Case Complexity of Efficient Global Optimization. [PDF]

J Optim Theory Appl
Xu W, Jiang Y, Maddalena ET, Jones CN.
europepmc   +1 more source

The dynamical Ising-Kac model in 3D converges to Φ 3 4. [PDF]

Probab Theory Relat Fields
Grazieschi P, Matetski K, Weber H.
europepmc   +1 more source

Local Well-Posedness of the Skew Mean Curvature Flow for Small Data in d≧2 Dimensions. [PDF]

Arch Ration Mech Anal
Huang J, Tataru D.
europepmc   +1 more source

Some of the next articles are maybe not open access.

Related searches:

Changes of Variable in Besov Spaces

Mathematische Nachrichten, 1999
AbstractWe characterize the set of all homeomorphisms π such that the mapping f → f o π is bounded on a homogeneous or inhomogeneous Besov space (0 < s < 1).
Gérard Bourdaud, Winfried Sickel
exaly   +3 more sources

Hydrodynamics in Besov Spaces

Archive for Rational Mechanics and Analysis, 1998
The Euler equation of an ideal incompressible fluid in borderline Besov spaces is studied. The novelty of this study is the logarithmic inequality which implies global existence in time for a solution with vorticity in a certain functional space.
openaire   +2 more sources

On Generalized Besov and Campanato Spaces

Ukrainian Mathematical Journal, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Rzaev, R. M., Gakhramanova, Z. Sh., Alieva, L. R.   +2 more
openaire   +1 more source

Interpolating Sequences for Besov Spaces

Journal of Functional Analysis, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly   +3 more sources

Logspline Deconvolution in Besov Space

Scandinavian Journal of Statistics, 1999
ABSTRACT. In this paper we consider logspline density estimation for random variables which are contaminated with random noise. In the logspline density estimation for data without noise, the logarithm of an unknown density function is estimated by a polynomial spline, the unknown parameters of which are given by maximum likelihood.
openaire   +2 more sources