9 citations · 36 across the 14 of their papers we have counts for
8 papers · 1 filter
On Training and Evaluation of Neural Network Approaches for Model Predictive Control
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
The contribution of this paper is a framework for training and evaluation of Model Predictive Control (MPC) implemented using constrained neural networks. Recent studies have propo…
Learning sparse linear dynamic networks in a hyper-parameter free setting
Arun Venkitaraman, Håkan Hjalmarsson, Bo Wahlberg
We address the issue of estimating the topology and dynamics of sparse linear dynamic networks in a hyperparameter-free setting. We propose a method to estimate the network dynamic…
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…
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…
Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
Arun Venkitaraman, Dave Zachariah
We address the problem of prediction of multivariate data process using an underlying graph model. We develop a method that learns a sparse partial correlation graph in a tuning-fr…
A Connectedness Constraint for Learning Sparse Graphs
Martin Sundin, Arun Venkitaraman, Magnus Jansson +1
Graphs are naturally sparse objects that are used to study many problems involving networks, for example, distributed learning and graph signal processing. In some cases, the graph…