9 citations · 36 across the 14 of their papers we have counts for
5 papers · 1 filter
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…
Supervised Linear Regression for Graph Learning from Graph Signals
Arun Venkitaraman, Hermina Petric Maretic, Saikat Chatterjee +1
We propose a supervised learning approach for predicting an underlying graph from a set of graph signals. Our approach is based on linear regression. In the linear regression model…
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…
R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network
Arun Venkitaraman, Alireza M. Javid, Saikat Chatterjee
We consider a neural network architecture with randomized features, a sign-splitter, followed by rectified linear units (ReLU). We prove that our architecture exhibits robustness t…