7 citations · 17 across the 5 of their papers we have counts for
5 papers
An Effective Data-Driven Approach for Localizing Deep Learning Faults
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their s…
Characterizing Bugs in Python and R Data Analytics Programs
Shibbir Ahmed, Mohammad Wardat, Hamid Bagheri +2
R and Python are among the most popular languages used in many critical data analytics tasks. However, we still do not fully understand the capabilities of these two languages w.r.…
Decomposing a Recurrent Neural Network into Modules for Enabling Reusability and Replacement
Sayem Mohammad Imtiaz, Fraol Batole, Astha Singh +3
Can we take a recurrent neural network (RNN) trained to translate between languages and augment it to support a new natural language without retraining the model from scratch? Can…
An Empirical Study on the Bugs Found while Reusing Pre-trained Natural Language Processing Models
Rangeet Pan, Sumon Biswas, Mohna Chakraborty +2
In NLP, reusing pre-trained models instead of training from scratch has gained popularity; however, NLP models are mostly black boxes, very large, and often require significant res…
DeepDiagnosis: Automatically Diagnosing Faults and Recommending Actionable Fixes in Deep Learning Programs
Mohammad Wardat, Breno Dantas Cruz, Wei Le +1
Deep Neural Networks (DNNs) are used in a wide variety of applications. However, as in any software application, DNN-based apps are afflicted with bugs. Previous work observed that…