no code implementations • 3 Apr 2023 • Qinyue Zheng, Arun Venkitaraman, Simona Petravic, Pascal Frossard
We consider two cases (a) when a single student is learnt for all the patients using preselected channels; and (b) when personalized students are learnt for every individual patient, with personalized channel selection using a Gumbelsoftmax approach.
no code implementations • 1 Nov 2022 • 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.
no code implementations • 3 Feb 2021 • Rebecka Winqvist, Arun Venkitaraman, Bo Wahlberg
As a proof of concept, we apply this approach to explicit MPC (eMPC), for which the feedback law is a piece-wise affine function of the state, but the number of pieces grows rapidly with the state dimension.
no code implementations • 12 Jun 2020 • Arun Venkitaraman, Anders Hansson, Bo Wahlberg
Our hypothesis is that the use of tasksimilarity helps meta-learning when the available tasks are limited and may contain outlier/ dissimilar tasks.
no code implementations • 8 May 2020 • 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.
no code implementations • 7 May 2020 • Alireza M. Javid, Xinyue Liang, Arun Venkitaraman, Saikat Chatterjee
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.
no code implementations • 29 Mar 2020 • Alireza M. Javid, Arun Venkitaraman, Mikael Skoglund, Saikat Chatterjee
We show that the proposed architecture is norm-preserving and provides an invertible feature vector, and therefore, can be used to reduce the training cost of any other learning method which employs linear projection to estimate the target.
no code implementations • 26 Nov 2019 • 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.
no code implementations • 26 Nov 2019 • 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.
no code implementations • 6 Nov 2018 • 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.
no code implementations • 15 Mar 2018 • 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.
no code implementations • 12 Mar 2018 • 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.
no code implementations • 12 Mar 2018 • 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).
no code implementations • 12 Mar 2018 • Arun Venkitaraman, Saikat Chatterjee, Peter Händel
In this article, we improve extreme learning machines for regression tasks using a graph signal processing based regularization.
no code implementations • 12 Dec 2017 • Arun Venkitaraman, Dave Zachariah
We address the problem of prediction of multivariate data process using an underlying graph model.
1 code implementation • 29 Aug 2017 • Martin Sundin, Arun Venkitaraman, Magnus Jansson, Saikat Chatterjee
We especially show how the constraint relates to the distributed consensus problem and graph Laplacian learning.