Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Valentin De Bortoli"'
In this work we introduce a statistical framework in order to analyze the spatial redundancy in natural images. This notion of spatial redundancy must be defined locally and thus we give some examples of functions (auto-similarity and template simila
Externí odkaz:
http://arxiv.org/abs/1904.06428
In this article we consider macrocanonical models for texture synthesis. In these models samples are generated given an input texture image and a set of features which should be matched in expectation. It is known that if the images are quantized, ma
Externí odkaz:
http://arxiv.org/abs/1904.06396
Publikováno v:
ICASSP
45th International Conference on Acoustics, Speech, and Signal Processing
45th International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain
45th International Conference on Acoustics, Speech, and Signal Processing
45th International Conference on Acoustics, Speech, and Signal Processing, May 2020, Barcelona, Spain
Approximate Bayesian Computation (ABC) is a popular method for approximate inference in generative models with intractable but easy-to-sample likelihood. It constructs an approximate posterior distribution by finding parameters for which the simulate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c78058c7dad43f958916fa052521763
https://hal.science/hal-03945515
https://hal.science/hal-03945515
Autor:
Joseph L. Watson, David Juergens, Nathaniel R. Bennett, Brian L. Trippe, Jason Yim, Helen E. Eisenach, Woody Ahern, Andrew J. Borst, Robert J. Ragotte, Lukas F. Milles, Basile I. M. Wicky, Nikita Hanikel, Samuel J. Pellock, Alexis Courbet, William Sheffler, Jue Wang, Preetham Venkatesh, Isaac Sappington, Susana Vázquez Torres, Anna Lauko, Valentin De Bortoli, Emile Mathieu, Regina Barzilay, Tommi S. Jaakkola, Frank DiMaio, Minkyung Baek, David Baker
There has been considerable recent progress in designing new proteins using deep learning methods1–9. Despite this progress, a general deep learning framework for protein design that enables solution of a wide range of design challenges, includingd
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::33b3dc2111101afaf7ffd55bbbc5fd12
https://doi.org/10.1101/2022.12.09.519842
https://doi.org/10.1101/2022.12.09.519842
Publikováno v:
SIAM Journal on Mathematics of Data Science
SIAM Journal on Mathematics of Data Science, Society for Industrial and Applied Mathematics, 2020, ⟨10.1137/19M1307731⟩
SIAM Journal on Mathematics of Data Science, Society for Industrial and Applied Mathematics, 2020, ⟨10.1137/19M1307731⟩
International audience; Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis. These methods often rely on the minimization of some variational formulation on the image space for which the minimi
Publikováno v:
SIAM Journal on Imaging Sciences. 13:1945-1989
Many imaging problems require solving an inverse problem that is ill-conditioned or ill-posed. Imaging methods typically address this difficulty by regularising the estimation problem to make it well-posed. This often requires setting the value of th
Autor:
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
Publikováno v:
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision, 2023, 65, pp.140-163. ⟨10.1007/s10851-022-01134-7⟩
Journal of Mathematical Imaging and Vision, 2023, 65, pp.140-163. ⟨10.1007/s10851-022-01134-7⟩
International audience; Bayesian methods to solve imaging inverse problems usually combine an explicit data likelihood function with a prior distribution that explicitly models expected properties of the solution. Many kinds of priors have been explo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e16589f8ee7a2daf93a9607a478f6257
Autor:
Rémi Laumont, Valentin De Bortoli, Andrés Almansa, Julie Delon, Alain Durmus, Marcelo Pereyra
Publikováno v:
SIAM Journal on Imaging Sciences
SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2022
SIAM Journal on Imaging Sciences, 2022, ⟨10.1137/21m1406349⟩
SIAM Journal on Imaging Sciences, Society for Industrial and Applied Mathematics, 2022
SIAM Journal on Imaging Sciences, 2022, ⟨10.1137/21m1406349⟩
Since the seminal work of Venkatakrishnan et al. [83] in 2013, Plug & Play (PnP) methods have become ubiquitous in Bayesian imaging. These methods derive Minimum Mean Square Error (MMSE) or Maximum A Posteriori (MAP) estimators for inverse problems i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe08476ef653b12aabef7584e120e96b
https://hal.archives-ouvertes.fr/hal-03161400v2/file/main(1).pdf
https://hal.archives-ouvertes.fr/hal-03161400v2/file/main(1).pdf
Publikováno v:
Statistics and Computing. 31
Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in problems with missing or incomplete data, and in parametric empirical B
Publikováno v:
ESAIM: Probability and Statistics
ESAIM: Probability and Statistics, EDP Sciences, 2020, Vol. 24, pp. 627-660
ESAIM: Probability and Statistics, EDP Sciences, 2020, 24, pp.627-660. ⟨10.1051/ps/2020010⟩
ESAIM: Probability and Statistics, EDP Sciences, 2020, Vol. 24, pp. 627-660
ESAIM: Probability and Statistics, EDP Sciences, 2020, 24, pp.627-660. ⟨10.1051/ps/2020010⟩
International audience; We introduce and study a notion of spatial redundancy in Gaussian random fields. we define similarity functions with some properties and give insight about their statistical properties in the context of image processing. We co
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79666ff64b0aa54d63a831992e6588d0
https://hal.archives-ouvertes.fr/hal-01931737
https://hal.archives-ouvertes.fr/hal-01931737