Most recent progress in natural language understanding (NLU) has been driven, in part, by
benchmarks such as GLUE, SuperGLUE, SQuAD, etc. In fact, many NLU models have now …

Paraphrase generation is a longstanding NLP task that has diverse applications for
downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies …

In semiconductor manufacturing, classification of defect patterns in wafer bin maps (WBMs)
helps engineers detect process failures and identify their causes. In recent studies on …

Recently, video recognition is emerging with the help of multi-modal learning, which focuses
on integrating distinct modalities to improve the performance or robustness of the model …

Noisy-label data inevitably gives rise to confusion in various perception applications. In this
work, we revisit the theory of support vector machines (SVM) which mines support vectors to …

Unsupervised domain adaptation (UDA) attempts to learn domain invariant representations
and has achieved significant progress, whereas self-training-based UDA methods have …

Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning
on graphs, but they often suffer from overfitting and label noise issues, especially when the …

The presence of corrupted labels is a common problem in the medical image datasets due
to the difficulty of annotation. Meanwhile, corrupted labels might significantly deteriorate the …

As a new paradigm in training intelligent models, federated learning is widely used to train a
global model without requiring local data to be uploaded from end devices. However, there …

Noisy labels present a significant challenge in deep learning because models are prone to
overfitting. This problem has driven the development of sophisticated techniques to address …