Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Arun Venkitaraman"'
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-19 (2020)
Abstract We design a rectified linear unit-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 a reduction of the training cost as the num
Externí odkaz:
https://doaj.org/article/6d243ccdf0b54f84a5b0380d1b9b98fc
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks. 5:698-710
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 quantities. In particular, the input may not be a graph signal at all or
Publikováno v:
Signal Processing. 156:106-115
We propose Hilbert transform and analytic signal construction for signals over graphs. This is motivated by the popularity of Hilbert transform, analytic signal, and modulation analysis in conventi ...
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 the gradient information in the training process. This is motivated b
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::df02ef68480d2d0aa1c67b4dfab7baed
http://arxiv.org/abs/2102.02173
http://arxiv.org/abs/2102.02173
Publikováno v:
EURASIP Journal on Advances in Signal Processing, Vol 2020, Iss 1, Pp 1-19 (2020)
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 a reduction of the training cost as the number of layers increases. L
Publikováno v:
ICASSP
We design a ReLU-based multilayer neural network to generate a rich high-dimensional feature vector. The feature guarantees a monotonically decreasing training cost as the number of layers increases. We design the weight matrix in each layer to exten
Publikováno v:
IEEE Transactions on Signal and Information Processing over Networks. 4:424-435
In this paper, we develop a greedy algorithm for solving the problem of sparse learning over a right stochastic network in a distributed manner. The nodes iteratively estimate the sparse signal by exchanging a weighted version of their individual int
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 dynamics in a computationally efficient and parameter tuning-free iterative f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::31a8646fc104dc48ed09a5cec730753e
http://arxiv.org/abs/1911.11553
http://arxiv.org/abs/1911.11553
Publikováno v:
ICASSP
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 either with missing samples or large perturbations. The presence of s
Publikováno v:
ICASSP
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 real-world problems, the graph expands over time as new nodes get introdu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc1a52a053619ac2b46a09f2f7799c96