Zobrazeno 1 - 10
of 304
pro vyhledávání: '"Marchand, Éric"'
In this article, we develop a new class of multivariate distributions adapted for count data, called Tree P{\'o}lya Splitting. This class results from the combination of a univariate distribution and singular multivariate distributions along a fixed
Externí odkaz:
http://arxiv.org/abs/2404.19528
For exponentially distributed lifetimes, we consider the prediction of future order statistics based on having observed the first $m$ order statistics. We focus on the previously less explored aspects of predicting: (i) an arbitrary pair of future or
Externí odkaz:
http://arxiv.org/abs/2403.06718
Autor:
Allard, Christine, Marchand, Éric
We study the problem of loss estimation that involves for an observable $X \sim f_{\theta}$ the choice of a first-stage estimator $\hat{\gamma}$ of $\gamma(\theta)$, incurred loss $L=L(\theta, \hat{\gamma})$, and the choice of a second-stage estimato
Externí odkaz:
http://arxiv.org/abs/2312.12149
Count data are omnipresent in many applied fields, often with overdispersion. With mixtures of Poisson distributions representing an elegant and appealing modelling strategy, we focus here on how the tail behaviour of the mixing distribution is relat
Externí odkaz:
http://arxiv.org/abs/2305.17095
This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip. To this end, we rely on an implicit-neural-representation (INR
Externí odkaz:
http://arxiv.org/abs/2303.15427
Autor:
Bhagwat, Pankaj, Marchand, Eric
This paper addresses the problem of an efficient predictive density estimation for the density $q(\|y-\theta\|^2)$ of $Y$ based on $X \sim p(\|x-\theta\|^2)$ for $y, x, \theta \in \mathbb{R}^d$. The chosen criteria are integrated $L_1$ loss given by
Externí odkaz:
http://arxiv.org/abs/2210.00972
Most classical SLAM systems rely on the static scene assumption, which limits their applicability in real world scenarios. Recent SLAM frameworks have been proposed to simultaneously track the camera and moving objects. However they are often unable
Externí odkaz:
http://arxiv.org/abs/2209.07888
Classical visual simultaneous localization and mapping (SLAM) algorithms usually assume the environment to be rigid. This assumption limits the applicability of those algorithms as they are unable to accurately estimate the camera poses and world str
Externí odkaz:
http://arxiv.org/abs/2202.12384
Autor:
Bhagwat, Pankaj, Marchand, Eric
We study frequentist risk properties of predictive density estimators for mean mixtures of multivariate normal distributions, involving an unknown location parameter $\theta \in \mathbb{R}^d$, and which include multivariate skew normal distributions.
Externí odkaz:
http://arxiv.org/abs/2202.00629
We propose a new SLAM system that uses the semantic segmentation of objects and structures in the scene. Semantic information is relevant as it contains high level information which may make SLAM more accurate and robust. Our contribution is twofold:
Externí odkaz:
http://arxiv.org/abs/2109.07339