Zobrazeno 1 - 10
of 4 736
pro vyhledávání: '"Fernández, Carlos"'
Autor:
Wagner, Royden, Tas, Ömer Sahin, Steiner, Marlon, Konstantinidis, Fabian, Königshof, Hendrik, Klemp, Marvin, Fernandez, Carlos, Stiller, Christoph
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model for forecasting scene-wide motion modes of multiple traffic agents. O
Externí odkaz:
http://arxiv.org/abs/2408.01537
In this paper, we study a nonlinear cointegration-type model of the form \(Z_t = f_0(X_t) + W_t\) where \(f_0\) is a monotone function and \(X_t\) is a Harris recurrent Markov chain. We use a nonparametric Least Square Estimator to locally estimate \
Externí odkaz:
http://arxiv.org/abs/2407.05294
It is the purpose of this paper to investigate the issue of estimating the regularity index $\beta>0$ of a discrete heavy-tailed r.v. $S$, \textit{i.e.} a r.v. $S$ valued in $\mathbb{N}^*$ such that $\mathbb{P}(S>n)=L(n)\cdot n^{-\beta}$ for all $n\g
Externí odkaz:
http://arxiv.org/abs/2407.05281
Autor:
Fernández, Carlos
Two regeneration-based bootstrap methods, namely, the \textit{Regeneration based-bootstrap} \cite{AthreyaFuh1992, Somnat-1993} and the \textit{Regenerative Block bootstrap} \cite{Bertail2006} are shown to be valid for the problem of estimating the in
Externí odkaz:
http://arxiv.org/abs/2407.05284
Autor:
Fernández, Carlos, Clémençon, Stephan
The main purpose of this article to extend the notion of statistical depth to the case of sample paths of a Markov chain. Initially introduced to define a center-outward ordering of points in the support of a multivariate distribution, depth function
Externí odkaz:
http://arxiv.org/abs/2406.16759
We demonstrated the potential of the fractional dimensional approach to understand exciton parameters in the exemplary atomically thin semiconductor material, a monolayer of WS$_2$. This approach has proved to be successful in finding the exciton bin
Externí odkaz:
http://arxiv.org/abs/2403.11579
We present JointMotion, a self-supervised pre-training method for joint motion prediction in self-driving vehicles. Our method jointly optimizes a scene-level objective connecting motion and environments, and an instance-level objective to refine lea
Externí odkaz:
http://arxiv.org/abs/2403.05489