2 citations · 3 across the 18 of their papers we have counts for
4 papers · 1 filter
Exploring Membership Inference Vulnerabilities in Clinical Large Language Models
Alexander Nemecek, Zebin Yun, Zahra Rahmani +4
As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems, ensuring their privacy and trustwor…
Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Biyao Zhang, Mingkai Zheng, Debargha Ganguly +4
Training Large Language Models(LLMs) is one of the most compute-intensive tasks in high-performance computing. Predicting end-to-end training time for multi-billion parameter model…
LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision
Debargha Ganguly, Sumit Kumar, Ishwar Balappanawar +6
Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost wh…
: Online Log Anomaly Detection Via Unsupervised Typicality Learning
Weicong Chen, Vikash Singh, Zahra Rahmani +3
Existing Log Anomaly Detection (LogAD) methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. We introduce , an unsupervised and p…