3 papers
cs.SE2026
woma: a real-time foundation model and its fine-tuned models for endoscopy
Thang Tran, Lan Dang
woma is a real-time foundation model for gastrointestinal endoscopy: a network trained without labels on about a million endoscopy frames, from which task models are fine-tuned. We…
cs.SE2026
Numbat: Building and Verifying a Self-Contained Machine-Learning Stack
Thang Tran, Lan Dang
Machine-learning systems are built almost exclusively on a few large Python-orchestrated frameworks, and they inherit those stacks' engineering costs: environments of hundreds of v…
cs.SE2026
Cross-Stack Validation of Language-Model Training: A Clinical Fine-Tuning Case Study
Thang Tran, Lan Dang
Neural network training has an oracle problem: a run can converge normally and yield a usable model while the software beneath it computes something other than specified. Almost al…