23 citations · 47 across the 14 of their papers we have counts for
9 papers · 1 filter
DeepBayes -- an estimator for parameter estimation in stochastic nonlinear dynamical models
Anubhab Ghosh, Mohamed Abdalmoaty, Saikat Chatterjee +1
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-world applications. Yet, estimating the unknown parameters of stochastic, nonlinear dynamical models remains a…
Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise
Arun Venkitaraman, Pascal Frossard, Saikat Chatterjee
In presence of sparse noise we propose kernel regression for predicting output vectors which are smooth over a given graph. Sparse noise models the training outputs being corrupted…
Locally Convex Sparse Learning over Networks
Ahmed Zaki, Saikat Chatterjee, Partha P. Mitra +1
We consider a distributed learning setup where a sparse signal is estimated over a network. Our main interest is to save communication resource for information exchange over the ne…
Gaussian Processes Over Graphs
Arun Venkitaraman, Saikat Chatterjee, Peter Händel
We propose Gaussian processes for signals over graphs (GPG) using the apriori knowledge that the target vectors lie over a graph. We incorporate this information using a graph- Lap…
Multi-kernel Regression For Graph Signal Processing
Arun Venkitaraman, Saikat Chatterjee, Peter Händel
We develop a multi-kernel based regression method for graph signal processing where the target signal is assumed to be smooth over a graph. In multi-kernel regression, an effective…
Extreme Learning Machine for Graph Signal Processing
Arun Venkitaraman, Saikat Chatterjee, Peter Händel
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization. We assume that the target signal for prediction or…