activity
20192022
most citedContrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

35 citations · 69 across the 5 of their papers we have counts for

collaborators

6 papers

cs.CL20221 cited

Overcoming Barriers to Skill Injection in Language Modeling: Case Study in Arithmetic

Mandar Sharma, Nikhil Muralidhar, Naren Ramakrishnan

Through their transfer learning abilities, highly-parameterized large pre-trained language models have dominated the NLP landscape for a multitude of downstream language tasks. Tho…

cs.CR2022

Detecting Irregular Network Activity with Adversarial Learning and Expert Feedback

Gopikrishna Rathinavel, Nikhil Muralidhar, Timothy O'Shea +1

Anomaly detection is a ubiquitous and challenging task relevant across many disciplines. With the vital role communication networks play in our daily lives, the security of these n…

cs.LG202235 cited

Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Cores

Nikhil Muralidhar, Abdullah Zubair, Nathanael Weidler +2

The availability of wide-ranging third-party intellectual property (3PIP) cores enables integrated circuit (IC) designers to focus on designing high-level features in ASICs/SoCs. T…

cs.LG202116 cited

Using AntiPatterns to avoid MLOps Mistakes

Nikhil Muralidhar, Sathappah Muthiah, Patrick Butler +8

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are p…

cs.LG2020

Steering a Historical Disease Forecasting Model Under a Pandemic: Case of Flu and COVID-19

Alexander Rodríguez, Nikhil Muralidhar, Bijaya Adhikari +3

Forecasting influenza in a timely manner aids health organizations and policymakers in adequate preparation and decision making. However, effective influenza forecasting still rema…

cs.LG201917 cited

Physics-guided Design and Learning of Neural Networks for Predicting Drag Force on Particle Suspensions in Moving Fluids

Nikhil Muralidhar, Jie Bu, Ze Cao +4

Physics-based simulations are often used to model and understand complex physical systems and processes in domains like fluid dynamics. Such simulations, although used frequently,…