
Powering Hidden Markov Model by Neural Network based Generative Models
Hidden Markov model (HMM) has been successfully used for sequential data...
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Neural Network based Explicit Mixture Models and Expectationmaximization based Learning
We propose two neural network based mixture models in this article. The ...
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Entropyregularized Optimal Transport Generative Models
We investigate the use of entropyregularized optimal transport (EOT) co...
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α Belief Propagation as Fully Factorized Approximation
Belief propagation (BP) can do exact inference in loopfree graphs, but ...
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Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit
We investigate an existing distributed algorithm for learning sparse sig...
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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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Bayesian Learning for LowRank matrix reconstruction
We develop latent variable models for Bayesian learning based lowrank m...
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Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine
Using a Bayesian approach, we consider the problem of recovering sparse ...
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Progressive Learning for Systematic Design of Large Neural Networks
We develop an algorithm for systematic design of a large artificial neur...
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Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events
Apnea, bradycardia and desaturation (ABD) events often precede lifethre...
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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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Locally Convex Sparse Learning over Networks
We consider a distributed learning setup where a sparse signal is estima...
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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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SSFN: Self Sizeestimating Feedforward Network and Low Complexity Design
We design a self sizeestimating feedforward network (SSFN) using a joi...
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Hidden Markov Models for sepsis detection in preterm infants
We explore the use of traditional and contemporary hidden Markov models ...
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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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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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Asynchronous Decentralized Learning of a Neural Network
In this work, we exploit an asynchronous computing framework namely ARoc...
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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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