Zobrazeno 1 - 10
of 6 159
pro vyhledávání: '"A. Pennec"'
Publikováno v:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-1-2023, Pp 277-284 (2023)
There is a natural tendency from the remote sensing community to extract area statistics (i.e. “Pixel counting”) from EO based geospatial products to produce statistical indicators for various purposes. However, geospatial map products suffer fro
Externí odkaz:
https://doaj.org/article/6ac42fa42e804c7399bb313477f50306
Valley photonic crystals provide efficient designs for the routing of light through channels in extremely compact geometries. The topological origin of the robust transport and the specific geometries under which it can take place have been questione
Externí odkaz:
http://arxiv.org/abs/2408.10294
Autor:
Sanborn, Sophia, Mathe, Johan, Papillon, Mathilde, Buracas, Domas, Lillemark, Hansen J, Shewmake, Christian, Bertics, Abby, Pennec, Xavier, Miolane, Nina
The enduring legacy of Euclidean geometry underpins classical machine learning, which, for decades, has been primarily developed for data lying in Euclidean space. Yet, modern machine learning increasingly encounters richly structured data that is in
Externí odkaz:
http://arxiv.org/abs/2407.09468
Many classic Reinforcement Learning (RL) algorithms rely on a Bellman operator, which involves an expectation over the next states, leading to the concept of bootstrapping. To introduce a form of pessimism, we propose to replace this expectation with
Externí odkaz:
http://arxiv.org/abs/2406.04081
Publikováno v:
Physical Review Research 6, 033284 (2024)
We propose a scheme to enhance the sensitivity of Non-Hermitian optomechanical mass-sensors. The benchmark system consists of two coupled optomechanical systems where the mechanical resonators are mechanically coupled. The optical cavities are driven
Externí odkaz:
http://arxiv.org/abs/2312.05057
Autor:
Szwagier, Tom, Pennec, Xavier
This paper raises an important issue about the interpretation of principal component analysis. The curse of isotropy states that a covariance matrix with repeated eigenvalues yields rotation-invariant eigenvectors. In other words, principal component
Externí odkaz:
http://arxiv.org/abs/2307.15348
In this paper we demonstrate how sub-Riemannian geometry can be used for manifold learning and surface reconstruction by combining local linear approximations of a point cloud to obtain lower dimensional bundles. Local approximations obtained by loca
Externí odkaz:
http://arxiv.org/abs/2307.03128
Autor:
Buet, Blanche, Pennec, Xavier
By interpreting the product of the Principal Component Analysis, that is the covariance matrix, as a sequence of nested subspaces naturally coming with weights according to the level of approximation they provide, we are able to embed all $d$--dimens
Externí odkaz:
http://arxiv.org/abs/2305.10583
Autor:
Rabenoro, Dimbihery, Pennec, Xavier
Consider a smooth manifold and an action on it of a compact connected Lie group with a bi-invariant metric. Then, any orbit is an embedded submanifold that is isometric to a normal homogeneous space for the group. In this paper, we establish new expl
Externí odkaz:
http://arxiv.org/abs/2302.14810
We study the sample complexity of obtaining an $\epsilon$-optimal policy in \emph{Robust} discounted Markov Decision Processes (RMDPs), given only access to a generative model of the nominal kernel. This problem is widely studied in the non-robust ca
Externí odkaz:
http://arxiv.org/abs/2302.05372