28 citations · 50 across the 9 of their papers we have counts for
9 papers
Accelerating Production LLMs with Combined Token/Embedding Speculators
Davis Wertheimer, Joshua Rosenkranz, Thomas Parnell +4
This technical report describes the design and training of novel speculative decoding draft models, for accelerating the inference speeds of large language models in a production e…
Automated Code generation for Information Technology Tasks in YAML through Large Language Models
Saurabh Pujar, Luca Buratti, Xiaojie Guo +8
The recent improvement in code generation capabilities due to the use of large language models has mainly benefited general purpose programming languages. Domain specific languages…
Study of Distractors in Neural Models of Code
Md Rafiqul Islam Rabin, Aftab Hussain, Sahil Suneja +1
Finding important features that contribute to the prediction of neural models is an active area of research in explainable AI. Neural models are opaque and finding such features sh…
VELVET: a noVel Ensemble Learning approach to automatically locate VulnErable sTatements
Yangruibo Ding, Sahil Suneja, Yunhui Zheng +4
Automatically locating vulnerable statements in source code is crucial to assure software security and alleviate developers' debugging efforts. This becomes even more important in…
Data-Driven AI Model Signal-Awareness Enhancement and Introspection
Sahil Suneja, Yufan Zhuang, Yunhui Zheng +2
AI modeling for source code understanding tasks has been making significant progress, and is being adopted in production development pipelines. However, reliability concerns, espec…
Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation
Yufan Zhuang, Sahil Suneja, Veronika Thost +3
Identifying vulnerable code is a precautionary measure to counter software security breaches. Tedious expert effort has been spent to build static analyzers, yet insecure patterns…
Probing Model Signal-Awareness via Prediction-Preserving Input Minimization
Sahil Suneja, Yunhui Zheng, Yufan Zhuang +2
This work explores the signal awareness of AI models for source code understanding. Using a software vulnerability detection use case, we evaluate the models' ability to capture th…
Learning to map source code to software vulnerability using code-as-a-graph
Sahil Suneja, Yunhui Zheng, Yufan Zhuang +2
We explore the applicability of Graph Neural Networks in learning the nuances of source code from a security perspective. Specifically, whether signatures of vulnerabilities in sou…
Secure Extensibility for System State Extraction via Plugin Sandboxing
Sahil Suneja, Canturk Isci
We introduce a new mechanism to securely extend systems data collection software with potentially untrusted third-party code. Unlike existing tools which run extension modules or p…