
Tasksimilarity Aware Metalearning through Nonparametric Kernel Regression
Metalearning refers to the process of abstracting a learning rule for a...
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On Training and Evaluation of Neural Network Approaches for Model Predictive Control
The contribution of this paper is a framework for training and evaluatio...
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Predictive Analysis of COVID19 Timeseries Data from Johns Hopkins University
We provide a predictive analysis of the spread of COVID19, also known a...
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Highdimensional Neural Feature using Rectified Linear Unit and Random Matrix Instance
We design a ReLUbased multilayer neural network to generate a rich high...
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Learning sparse linear dynamic networks in a hyperparameter free setting
We address the issue of estimating the topology and dynamics of sparse l...
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Recursive Prediction of Graph Signals with Incoming Nodes
Kernel and linear regression have been recently explored in the predicti...
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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise
In presence of sparse noise we propose kernel regression for predicting ...
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Supervised Linear Regression for Graph Learning from Graph Signals
We propose a supervised learning approach for predicting an underlying g...
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Gaussian Processes Over Graphs
We propose Gaussian processes for signals over graphs (GPG) using the ap...
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Multikernel Regression For Graph Signal Processing
We develop a multikernel based regression method for graph signal proce...
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Extreme Learning Machine for Graph Signal Processing
In this article, we improve extreme learning machines for regression tas...
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R3Net: Random Weights, Rectifier Linear Units and Robustness for Artificial Neural Network
We consider a neural network architecture with randomized features, a si...
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Learning Sparse Graphs for Prediction and Filtering of Multivariate Data Processes
We address the problem of prediction and filtering of multivariate data ...
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A Connectedness Constraint for Learning Sparse Graphs
Graphs are naturally sparse objects that are used to study many problems...
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Arun Venkitaraman
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