9 citations · 36 across the 14 of their papers we have counts for
18 papers
Knowledge-Distilled Graph Neural Networks for Personalized Epileptic Seizure Detection
Qinyue Zheng, Arun Venkitaraman, Simona Petravic +1
Wearable devices for seizure monitoring detection could significantly improve the quality of life of epileptic patients. However, existing solutions that mostly rely on full electr…
A Meta-GNN approach to personalized seizure detection and classification
Abdellah Rahmani, Arun Venkitaraman, Pascal Frossard
In this paper, we propose a personalized seizure detection and classification framework that quickly adapts to a specific patient from limited seizure samples. We achieve this by c…
Learning Models of Model Predictive Controllers using Gradient Data
Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
This paper investigates controller identification given data from a Model Predictive Controller (MPC) with constraints. We propose an approach for learning MPC that explicitly uses…
Task-similarity Aware Meta-learning through Nonparametric Kernel Regression
Arun Venkitaraman, Anders Hansson, Bo Wahlberg
This paper investigates the use of nonparametric kernel-regression to obtain a tasksimilarity aware meta-learning algorithm. Our hypothesis is that the use of tasksimilarity helps…
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
Alireza M. Javid, Xinyue Liang, Arun Venkitaraman +1
We provide a predictive analysis of the spread of COVID-19, also known as SARS-CoV-2, using the dataset made publicly available online by the Johns Hopkins University. Our main obj…
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…
High-dimensional Neural Feature Design for Layer-wise Reduction of Training Cost
Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund +1
We design a ReLU-based multilayer neural network by mapping the feature vectors to a higher dimensional space in every layer. We design the weight matrices in every layer to ensure…
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…
Recursive Prediction of Graph Signals with Incoming Nodes
Arun Venkitaraman, Saikat Chatterjee, Bo Wahlberg
Kernel and linear regression have been recently explored in the prediction of graph signals as the output, given arbitrary input signals that are agnostic to the graph. In many rea…
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…
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…
Predicting Graph Signals using Kernel Regression where the Input Signal is Agnostic to a Graph
Arun Venkitaraman, Saikat Chatterjee, Peter Händel
We propose a kernel regression method to predict a target signal lying over a graph when an input observation is given. The input and the output could be two different physical qua…
Hilbert Transform, Analytic Signal, and Modulation Analysis for Graph Signal Processing
Arun Venkitaraman, Saikat Chatterjee, Peter Händel
We propose Hilbert transform (HT) and analytic signal (AS) construction for signals over graphs. This is motivated by the popularity of HT, AS, and modulation analysis in conventio…