35 citations · 75 across the 12 of their papers we have counts for
6 papers · 1 filter
The Prompt is Mightier than the Example
Shengzhe Xu, Nikhil Muralidhar, Naren Ramakrishnan
Numerous recent prompt optimization approaches like chain-of-thought, have been demonstrated to significantly improve the quality of content generated by large language models (LLM…
Why LLMs Are Bad at Synthetic Table Generation (and what to do about it)
Shengzhe Xu, Cho-Ting Lee, Mandar Sharma +3
Synthetic data generation is integral to ML pipelines, e.g., to augment training data, replace sensitive information, and even to power advanced platforms like DeepSeek. While LLMs…
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…
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…
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…
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,…