9 papers
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…
Why Attention Fails: A Taxonomy of Faults in Attention-Based Neural Networks
Sigma Jahan, Saurabh Singh Rajput, Tushar Sharma +1
Attention mechanisms are at the core of modern neural architectures, powering systems ranging from ChatGPT to autonomous vehicles and driving a major economic impact. However, high…
Can Hessian-Based Insights Support Fault Diagnosis in Attention-based Models?
Sigma Jahan, Mohammad Masudur Rahman
As attention-based deep learning models scale in size and complexity, diagnosing their faults becomes increasingly challenging. In this work, we conduct an empirical study to evalu…
Improved Detection and Diagnosis of Faults in Deep Neural Networks Using Hierarchical and Explainable Classification
Sigma Jahan, Mehil B Shah, Parvez Mahbub +1
Deep Neural Networks (DNN) have found numerous applications in various domains, including fraud detection, medical diagnosis, facial recognition, and autonomous driving. However, D…
Towards Understanding the Challenges of Bug Localization in Deep Learning Systems
Sigma Jahan, Mehil B. Shah, Mohammad Masudur Rahman
Software bugs cost the global economy billions of dollars annually and claim ~50\% of the programming time from software developers. Locating these bugs is crucial for their resolu…
Towards Understanding the Impacts of Textual Dissimilarity on Duplicate Bug Report Detection
Sigma Jahan, Mohammad Masudur Rahman
About 40% of software bug reports are duplicates of one another, which pose a major overhead during software maintenance. Traditional techniques often focus on detecting duplicate…