29 papers
A single design choice determines whether machine learning models of materials make physically impossible predictions
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Machine-learned models are replacing first-principles calculations across materials discovery, and physical symmetry is the central guarantee built into them. The debate over how m…
When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?
Can Polat, Mustafa Kurban, Erchin Serpedin +1
Machine-learned exchange-correlation functionals correct band gaps at near-semilocal cost, while density-functional tight binding reaches the --atom regime; combining t…
ConfTriage: A Calibration-Aware LLM Triage Framework for Pulmonary Nodule Malignancy with Selective Specialist Deferral
Md Rabiul Islam, Samir Abdaljalil, Erchin Serpedin +1
Pulmonary nodule malignancy prediction typically depends on image-trained specialist deep learning (DL) models that require substantial annotated imaging data and task-specific tra…
Conformal Coverage Guarantees for Any Video Temporal Grounder
Aseel Mohamed, Rasul Khanbayov, Erchin Serpedin +1
Event boundaries in continuous video are ambiguous: re-annotate the same query-video pair and independent annotators mark moments that overlap by less than half on a large fraction…
Consistency Has a Computable Blind Spot: A Commutation Theory of Label-Free Reliability for Vision-Language Figure Reading
Rasul Khanbayov, Hasan Kurban
Label-free reliability for vision-language models rests on invariance: perturb the input and a faithful reader's answer should not change. This has a known blind spot, a systematic…
When Does Consensus Mean Correctness? Measuring the Agreement-Accuracy Coupling with Semantics-Preserving Re-Rendering
Rasul Khanbayov, Hasan Kurban
A model's agreement across perturbed inputs is used both as a label-free reliability signal and as a self-training target, on the premise that agreement tracks correctness. That co…