activity
20172021
most citedDo optimization methods in deep learning applications matter?

5 citations · 6 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2021

Mining Scientific Workflows for Anomalous Data Transfers

Huy Tu, George Papadimitriou, Mariam Kiran +4

Modern scientific workflows are data-driven and are often executed on distributed, heterogeneous, high-performance computing infrastructures. Anomalies and failures in the workflow…

quant-ph2020

Towards AI-enabled Control for Enhancing Quantum Transduction

Mekena Metcalf, Anastasiia Butko, Mariam Kiran

With advent of quantum internet, it becomes crucial to find novel ways to connect distributed quantum testbeds and develop novel technologies and research that extend innovations i…

cs.LG2020

Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks

Tanwi Mallick, Mariam Kiran, Bashir Mohammed +1

Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities an…

cs.LG20205 cited

Do optimization methods in deep learning applications matter?

Buse Melis Ozyildirim, Mariam Kiran

With advances in deep learning, exponential data growth and increasing model complexity, developing efficient optimization methods are attracting much research attention. Several i…

cs.DC20171 cited

Technical Report on Deploying a highly secured OpenStack Cloud Infrastructure using BradStack as a Case Study

Bashir Mohammed, Sibusiso Moyo, K. M Maiyama +4

Cloud computing has emerged as a popular paradigm and an attractive model for providing a reliable distributed computing model.it is increasing attracting huge attention both in ac…