3 accepted papers
Ensuring consistently high-quality training data is essential for developing reliable machine learning systems. Recent research demonstrates that incorporating human supervision into training set debugging effectively improves model performance, especially for text classification tasks. However, suc
Adversarial training is an effective technique for enhancing the robustness of deep neural networks (DNNs). Prior research shows that misclassified examples influence final adversarial robustness much more than correctly classified examples. Ignoring this difference during training can hurt model pe
This paper presents a multi-granularity method for source code summarization, which generates a concise functional description for the given code snippet. We notice that skilled programmers write and read source codes hierarchically and pay close attention to conceptual entities like statements, tok…