Cross-modal retrieval (CMR) aims to establish interaction between different modalities,
among which supervised CMR is emerging due to its flexibility in learning semantic category …

Recently, deep learning models have been widely applied in program understanding tasks,
and these models achieve state-of-the-art results on many benchmark datasets. A major …

Training machine learning models from data with weak supervision and dataset shifts is still
challenging. Designing algorithms when these two situations arise has not been explored …

Data augmentation plays a pivotal role in enhancing and diversifying training data.
Nonetheless, consistently improving model performance in varied learning scenarios …

Deep neural networks (DNNs) have proven highly effective in various computational tasks,
but their success depends largely on access to large datasets with accurate labels …

We propose a meta-learning method to improve the anomaly detection performance on
unseen target tasks that have only unlabeled data. Existing meta-learning methods for …

Noisy label learning aims to learn robust networks under the supervision of noisy labels,
which plays a critical role in deep learning. Existing work either conducts sample selection …

Most real-world datasets contain label noise, which can negatively affect downstream ML
models trained on them. To deal with this problem, one can clean the mislabeled data …

Understanding human actions from body poses is critical for assistive robots sharing space
with humans in order to make informed and safe decisions about the next interaction …

Recently, deep learning models have been widely applied in program understanding tasks,
and these models achieve state-of-the-art results on many benchmark datasets. A major …