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20092025
most citedDecentralized Gaussian Filters for Cooperative Self-localization and Multi-target Tracking

58 citations · 176 across the 36 of their papers we have counts for

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Showing 2018Show all

13 papers · 1 filter

cs.LG2018

MR-GAN: Manifold Regularized Generative Adversarial Networks

Qunwei Li, Bhavya Kailkhura, Rushil Anirudh +3

Despite the growing interest in generative adversarial networks (GANs), training GANs remains a challenging problem, both from a theoretical and a practical standpoint. To address…

cs.IT2018

On the optimality of likelihood ratio test for prospect theory based binary hypothesis testing

Sinan Gezici, Pramod K. Varshney

In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizin…

cs.IT2018

Energy-efficient Decision Fusion for Distributed Detection in Wireless Sensor Networks

N. Sriranga, K. G. Nagananda, R. S. Blum +2

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection i…

cs.LG2018

K-medoids Clustering of Data Sequences with Composite Distributions

Tiexing Wang, Qunwei Li, Donald J. Bucci +3

This paper studies clustering of data sequences using the k-medoids algorithm. All the data sequences are assumed to be generated from \emph{unknown} continuous distributions, whic…

cs.IT2018

Abnormality Detection inside Blood Vessels with Mobile Nanomachines

Neeraj Varshney, Adarsh Patel, Yansha Deng +3

Motivated by the numerous healthcare applications of molecular communication within Internet of Bio-Nano Things (IoBNT), this work addresses the problem of abnormality detection in…

stat.ML2018

Why Interpretability in Machine Learning? An Answer Using Distributed Detection and Data Fusion Theory

Kush R. Varshney, Prashant Khanduri, Pranay Sharma +2

As artificial intelligence is increasingly affecting all parts of society and life, there is growing recognition that human interpretability of machine learning models is important…