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09/14/2023
Identifying and analysing toxic actors and communities on Facebook by employing network analysis
There has been an increasingly widespread agreement among both academic ...
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01/31/2023
Automated Sentiment and Hate Speech Analysis of Facebook Data by Employing Multilingual Transformer Models
In recent years, there has been a heightened consensus within academia a...
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12/19/2022
Observability-aware online multi-lidar extrinsic calibration
Accurate and robust extrinsic calibration is necessary for deploying aut...
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10/03/2022
M-LIO: Multi-lidar, multi-IMU odometry with sensor dropout tolerance
We present a robust system for state estimation that fuses measurements ...
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07/19/2022
Neural Greedy Pursuit for Feature Selection
We propose a greedy algorithm to select N important features among P inp...
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05/14/2022
Extrinsic Calibration and Verification of Multiple Non-overlapping Field of View Lidar Sensors
We demonstrate a multi-lidar calibration framework for large mobile plat...
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05/14/2022
Multi-modal curb detection and filtering
Reliable knowledge of road boundaries is critical for autonomous vehicle...
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05/04/2022
DeepBayes – an estimator for parameter estimation in stochastic nonlinear dynamical models
Stochastic nonlinear dynamical systems are ubiquitous in modern, real-wo...
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10/06/2021
Use of Deterministic Transforms to Design Weight Matrices of a Neural Network
Self size-estimating feedforward network (SSFN) is a feedforward multila...
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07/01/2021
Normalizing Flow based Hidden Markov Models for Classification of Speech Phones with Explainability
In pursuit of explainability, we develop generative models for sequentia...
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02/15/2021
Robust Classification using Hidden Markov Models and Mixtures of Normalizing Flows
We test the robustness of a maximum-likelihood (ML) based classifier whe...
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11/18/2020
Statistical model-based evaluation of neural networks
Using a statistical model-based data generation, we develop an experimen...
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10/22/2020
A ReLU Dense Layer to Improve the Performance of Neural Networks
We propose ReDense as a simple and low complexity way to improve the per...
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09/29/2020
A Low Complexity Decentralized Neural Net with Centralized Equivalence using Layer-wise Learning
We design a low complexity decentralized learning algorithm to train a r...
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05/07/2020
Predictive Analysis of COVID-19 Time-series Data from Johns Hopkins University
We provide a predictive analysis of the spread of COVID-19, also known a...
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04/10/2020
Asynchronous Decentralized Learning of a Neural Network
In this work, we exploit an asynchronous computing framework namely ARoc...
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03/29/2020
High-dimensional Neural Feature using Rectified Linear Unit and Random Matrix Instance
We design a ReLU-based multilayer neural network to generate a rich high...
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11/26/2019
Recursive Prediction of Graph Signals with Incoming Nodes
Kernel and linear regression have been recently explored in the predicti...
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10/30/2019
Hidden Markov Models for sepsis detection in preterm infants
We explore the use of traditional and contemporary hidden Markov models ...
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10/13/2019
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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08/23/2019
α Belief Propagation as Fully Factorized Approximation
Belief propagation (BP) can do exact inference in loop-free graphs, but ...
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07/31/2019
Neural Network based Explicit Mixture Models and Expectation-maximization based Learning
We propose two neural network based mixture models in this article. The ...
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05/17/2019
SSFN: Self Size-estimating Feed-forward Network and Low Complexity Design
We design a self size-estimating feed-forward network (SSFN) using a joi...
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11/16/2018
Entropy-regularized Optimal Transport Generative Models
We investigate the use of entropy-regularized optimal transport (EOT) co...
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11/06/2018
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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11/05/2018
Supervised Linear Regression for Graph Learning from Graph Signals
We propose a supervised learning approach for predicting an underlying g...
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03/31/2018
Locally Convex Sparse Learning over Networks
We consider a distributed learning setup where a sparse signal is estima...
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03/15/2018
Gaussian Processes Over Graphs
We propose Gaussian processes for signals over graphs (GPG) using the ap...
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03/12/2018
Multi-kernel Regression For Graph Signal Processing
We develop a multi-kernel based regression method for graph signal proce...
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03/12/2018
Extreme Learning Machine for Graph Signal Processing
In this article, we improve extreme learning machines for regression tas...
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03/12/2018
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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11/17/2017
Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events
Apnea, bradycardia and desaturation (ABD) events often precede life-thre...
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10/23/2017
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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09/22/2017
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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08/29/2017
A Connectedness Constraint for Learning Sparse Graphs
Graphs are naturally sparse objects that are used to study many problems...
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01/23/2015
Bayesian Learning for Low-Rank matrix reconstruction
We develop latent variable models for Bayesian learning based low-rank m...
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01/12/2015