no code implementations • 8 May 2024 • John-Joseph Brady, Yuhui Luo, Wenwu Wang, Victor Elvira, Yunpeng Li
Differentiable particle filters are an emerging class of models that combine sequential Monte Carlo techniques with the flexibility of neural networks to perform state space inference.
no code implementations • 21 Nov 2023 • Shalini Sharma, Angshul Majumdar, Emilie Chouzenoux, Victor Elvira
We call the proposed approach the deep state-space model.
no code implementations • 6 Jul 2023 • Emilie Chouzenoux, Victor Elvira
This work proposes a novel approach to fill this gap by introducing a joint graphical modeling framework that bridges the static graphical Lasso model and a causal-based graphical approach for the linear-Gaussian SSM.
no code implementations • 14 Dec 2022 • Peng Wu, Tales Imbiriba, Victor Elvira, Pau Closas
When data is only available in a distributed fashion or when different sensors are used to infer a quantity of interest, data fusion becomes essential.
1 code implementation • 3 Oct 2022 • Yunshi Huang, Emilie Chouzenoux, Victor Elvira, Jean-Christophe Pesquet
Bayesian neural networks (BNNs) have received an increased interest in the last years.
no code implementations • 12 Jul 2018 • Ömer Deniz Akyildiz, Victor Elvira, Joaquin Miguez
We then carry out this observation to a general sequential setting: We consider the incremental proximal method, which is an algorithm for large-scale optimization, and show that, for a linear-quadratic cost function, it can naturally be realized by the Kalman filter.
no code implementations • 16 Feb 2018 • Steven Van Vaerenbergh, Ignacio Santamaria, Victor Elvira, Matteo Salvatori
In this paper, we study the problem of locating a predefined sequence of patterns in a time series.
no code implementations • 15 Apr 2017 • Luca Martino, Victor Elvira
Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems.
no code implementations • 21 Nov 2016 • Luca Martino, Victor Elvira, Gustau Camps-Valls
The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions.