4 papers · 1 filter
Deep4ge: DNN Training Trajectories for Fault Detection and Diagnosis
Sigma Jahan
Deep learning systems often fail due to subtle implementation faults that alter training behavior. Recent work has studied how to detect and diagnose such failures from changes obs…
Toward Localizing and Repairing Bias in Transformer Attention Heads
Sigma Jahan
Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and…
Evaluation-Strategy Gap in Fault Diagnosis of Deep Learning Programs
Sigma Jahan
Deep Learning (DL) programs can fail during training for many reasons, and diagnosing the cause is a costly and time-consuming maintenance task. Techniques for diagnosing such fail…
Hierarchical Fault Detection and Diagnosis for Transformer Architectures
Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma +1
Transformers now underpin critical AI systems across industry and research. Yet their faults can silently alter model behavior without runtime errors, and existing techniques offer…