Zobrazeno 1 - 10
of 525
pro vyhledávání: '"Munk, Axel"'
Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a certain extent. Distribu
Externí odkaz:
http://arxiv.org/abs/2407.15297
Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separat
Externí odkaz:
http://arxiv.org/abs/2403.13197
The inverse optimal transport problem is to find the underlying cost function from the knowledge of optimal transport plans. While this amounts to solving a linear inverse problem, in this work we will be concerned with the nonlinear inverse problem
Externí odkaz:
http://arxiv.org/abs/2312.05843
Due to its computational complexity, graph cuts for cluster detection and identification are used mostly in the form of convex relaxations. We propose to utilize the original graph cuts such as Ratio, Normalized or Cheeger Cut in order to detect clus
Externí odkaz:
http://arxiv.org/abs/2308.09613
We develop a multiscale scanning method to find anomalies in a $d$-dimensional random field in the presence of nuisance parameters. This covers the common situation that either the baseline-level or additional parameters such as the variance are unkn
Externí odkaz:
http://arxiv.org/abs/2307.13301
Hidden Markov models (HMMs) are characterized by an unobservable (hidden) Markov chain and an observable process, which is a noisy version of the hidden chain. Decoding the original signal (i.e., hidden chain) from the noisy observations is one of th
Externí odkaz:
http://arxiv.org/abs/2305.18578
Optimal transport (OT) based data analysis is often faced with the issue that the underlying cost function is (partially) unknown. This paper is concerned with the derivation of distributional limits for the empirical OT value when the cost function
Externí odkaz:
http://arxiv.org/abs/2301.01287
We analyze statistical properties of plug-in estimators for unbalanced optimal transport quantities between finitely supported measures in different prototypical sampling models. Specifically, our main results provide non-asymptotic bounds on the exp
Externí odkaz:
http://arxiv.org/abs/2211.08858
Quantifying the number of molecules from fluorescence microscopy measurements is an important topic in cell biology and medical research. In this work, we present a consecutive algorithm for super-resolution (STED) scanning microscopy that provides m
Externí odkaz:
http://arxiv.org/abs/2207.13426
Autor:
Mordant, Gilles, Munk, Axel
In this paper, we consider a certain convolutional Laplacian for metric measure spaces and investigate its potential for the statistical analysis of complex objects. The spectrum of that Laplacian serves as a signature of the space under consideratio
Externí odkaz:
http://arxiv.org/abs/2204.06493