Saikat Chatterjee

research

∙ 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...

0 Ritumbra Manuvie, et al. ∙

research

∙ 12/19/2022

Observability-aware online multi-lidar extrinsic calibration

Accurate and robust extrinsic calibration is necessary for deploying aut...

0 Sandipan Das, et al. ∙

research

∙ 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 ...

0 Sandipan Das, et al. ∙

research

∙ 07/19/2022

Neural Greedy Pursuit for Feature Selection

We propose a greedy algorithm to select N important features among P inp...

0 Sandipan Das, et al. ∙

research

∙ 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...

0 Sandipan Das, et al. ∙

research

∙ 05/14/2022

Multi-modal curb detection and filtering

Reliable knowledge of road boundaries is critical for autonomous vehicle...

0 Sandipan Das, et al. ∙

research

∙ 05/04/2022

DeepBayes – an estimator for parameter estimation in stochastic nonlinear dynamical models

Stochastic nonlinear dynamical systems are ubiquitous in modern, real-wo...

0 Anubhab Ghosh, et al. ∙

research

∙ 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...

0 Pol Grau Jurado, et al. ∙

research

∙ 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...

0 Anubhab Ghosh, et al. ∙

research

∙ 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...

0 Anubhab Ghosh, et al. ∙

research

∙ 11/18/2020

Statistical model-based evaluation of neural networks

Using a statistical model-based data generation, we develop an experimen...

0 Sandipan Das, et al. ∙

research

∙ 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...

0 Alireza M. Javid, et al. ∙

research

∙ 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...

0 Xinyue Liang, et al. ∙

research

∙ 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...

0 Alireza M. Javid, et al. ∙

research

∙ 04/10/2020

Asynchronous Decentralized Learning of a Neural Network

In this work, we exploit an asynchronous computing framework namely ARoc...

0 Xinyue Liang, et al. ∙

research

∙ 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...

0 Alireza M. Javid, et al. ∙

research

∙ 11/26/2019

Recursive Prediction of Graph Signals with Incoming Nodes

Kernel and linear regression have been recently explored in the predicti...

0 Arun Venkitaraman, et al. ∙

research

∙ 10/30/2019

Hidden Markov Models for sepsis detection in preterm infants

We explore the use of traditional and contemporary hidden Markov models ...

0 Antoine Honoré, et al. ∙

research

∙ 10/13/2019

Powering Hidden Markov Model by Neural Network based Generative Models

Hidden Markov model (HMM) has been successfully used for sequential data...

32 Dong Liu, et al. ∙

research

∙ 08/23/2019

α Belief Propagation as Fully Factorized Approximation

Belief propagation (BP) can do exact inference in loop-free graphs, but ...

3 Dong Liu, et al. ∙

research

∙ 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 ...

5 Dong Liu, et al. ∙

research

∙ 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...

0 Saikat Chatterjee, et al. ∙

research

∙ 11/16/2018

Entropy-regularized Optimal Transport Generative Models

We investigate the use of entropy-regularized optimal transport (EOT) co...

4 Dong Liu, et al. ∙

research

∙ 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 ...

0 Arun Venkitaraman, et al. ∙

research

∙ 11/05/2018

Supervised Linear Regression for Graph Learning from Graph Signals

We propose a supervised learning approach for predicting an underlying g...

0 Arun Venkitaraman, et al. ∙

research

∙ 03/31/2018

Locally Convex Sparse Learning over Networks

We consider a distributed learning setup where a sparse signal is estima...

0 Ahmed Zaki, et al. ∙

research

∙ 03/15/2018

Gaussian Processes Over Graphs

We propose Gaussian processes for signals over graphs (GPG) using the ap...

0 Arun Venkitaraman, et al. ∙

research

∙ 03/12/2018

Multi-kernel Regression For Graph Signal Processing

We develop a multi-kernel based regression method for graph signal proce...

0 Arun Venkitaraman, et al. ∙

research

∙ 03/12/2018

Extreme Learning Machine for Graph Signal Processing

In this article, we improve extreme learning machines for regression tas...

0 Arun Venkitaraman, et al. ∙

research

∙ 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...

0 Arun Venkitaraman, et al. ∙

research

∙ 11/17/2017

Large Neural Network Based Detection of Apnea, Bradycardia and Desaturation Events

Apnea, bradycardia and desaturation (ABD) events often precede life-thre...

0 Antoine Honoré, et al. ∙

research

∙ 10/23/2017

Progressive Learning for Systematic Design of Large Neural Networks

We develop an algorithm for systematic design of a large artificial neur...

0 Saikat Chatterjee, et al. ∙

research

∙ 09/22/2017

Estimate Exchange over Network is Good for Distributed Hard Thresholding Pursuit

We investigate an existing distributed algorithm for learning sparse sig...

0 Ahmed Zaki, et al. ∙

research

∙ 08/29/2017

A Connectedness Constraint for Learning Sparse Graphs

Graphs are naturally sparse objects that are used to study many problems...

0 Martin Sundin, et al. ∙

research

∙ 01/23/2015

Bayesian Learning for Low-Rank matrix reconstruction

We develop latent variable models for Bayesian learning based low-rank m...

0 Martin Sundin, et al. ∙

research

∙ 01/12/2015

Combined modeling of sparse and dense noise for improvement of Relevance Vector Machine

Using a Bayesian approach, we consider the problem of recovering sparse ...

0 Martin Sundin, et al. ∙

Saikat Chatterjee

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