Zobrazeno 1 - 10
of 1 421
pro vyhledávání: '"Diepeveen, A."'
We propose Pullback Flow Matching (PFM), a novel framework for generative modeling on data manifolds. Unlike existing methods that assume or learn restrictive closed-form manifold mappings for training Riemannian Flow Matching (RFM) models, PFM lever
Externí odkaz:
http://arxiv.org/abs/2410.04543
Data-driven Riemannian geometry has emerged as a powerful tool for interpretable representation learning, offering improved efficiency in downstream tasks. Moving forward, it is crucial to balance cheap manifold mappings with efficient training algor
Externí odkaz:
http://arxiv.org/abs/2410.01950
Autor:
Diepeveen, N. A., Pereira, C. Robalo, Mazzanti, M., Ackerman, Z. E. D., Gallagher, L. P. H., Timmerman, T., Gerritsma, R., Schüssler, R. X.
We present spectroscopic data for four metastable state clear-out transitions between 399 nm and 412 nm for all even long-lived isotopes of Yb$^+$ as well as their hyperfine structure in $^{171}$Yb$^+$. The strong $^2 \rm{D}_{3/2} \rightarrow {}^1[1/
Externí odkaz:
http://arxiv.org/abs/2408.07380
Deep Learning-based Depth Estimation Methods from Monocular Image and Videos: A Comprehensive Survey
Estimating depth from single RGB images and videos is of widespread interest due to its applications in many areas, including autonomous driving, 3D reconstruction, digital entertainment, and robotics. More than 500 deep learning-based papers have be
Externí odkaz:
http://arxiv.org/abs/2406.19675
Autor:
Mazzanti, M., Pereira, C. Robalo, Diepeveen, N. A., Gerritsen, B., Wu, Z., Ackerman, Z. E. D., Gallagher, L. P. H., Safavi-Naini, A., Gerritsma, R., Schüssler, R. X.
This paper presents a routine to align an optical tweezer on a single trapped ion and use the ion as a probe to characterize the tweezer. We find a smallest tweezer waist of $2.3(2)\,\mu$m, which is in agreement with the theoretical minimal attainabl
Externí odkaz:
http://arxiv.org/abs/2406.06721
Autor:
Diepeveen, Willem
Data sets tend to live in low-dimensional non-linear subspaces. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The symmetric Riemannian geometry setting can be suitable for a variety of reasons. Fi
Externí odkaz:
http://arxiv.org/abs/2403.06612
Autor:
Diepeveen, Willem, Esteve-Yagüe, Carlos, Lellmann, Jan, Öktem, Ozan, Schönlieb, Carola-Bibiane
An increasingly common viewpoint is that protein dynamics data sets reside in a non-linear subspace of low conformational energy. Ideal data analysis tools for such data sets should therefore account for such non-linear geometry. The Riemannian geome
Externí odkaz:
http://arxiv.org/abs/2308.07818
When generalizing schemes for real-valued data approximation or decomposition to data living in Riemannian manifolds, tangent space-based schemes are very attractive for the simple reason that these spaces are linear. An open challenge is to do this
Externí odkaz:
http://arxiv.org/abs/2306.00507
Autor:
Kirsten Kruger, Yoou Myeonghyun, Nicky van der Wielen, Dieuwertje E. Kok, Guido J. Hooiveld, Shohreh Keshtkar, Marlies Diepeveen-de Bruin, Michiel G. J. Balvers, Mechteld Grootte-Bromhaar, Karin Mudde, Nhien T. H. N. Ly, Yannick Vermeiren, Lisette C. P. G. M. de Groot, Ric C. H. de Vos, Gerard Bryan Gonzales, Wilma T. Steegenga, Mara P. H. van Trijp
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Despite advances in gut health research, the variability of important gut markers within individuals over time remains underexplored. We investigated the intra-individual variation of various faecal gut health markers using an optimised proc
Externí odkaz:
https://doaj.org/article/b58a7adef63441b6876d1de3cbdac3af
We consider the problem of recovering the three-dimensional atomic structure of a flexible macromolecule from a heterogeneous cryo-EM dataset. The dataset contains noisy tomographic projections of the electrostatic potential of the macromolecule, tak
Externí odkaz:
http://arxiv.org/abs/2209.05546