Due to large specific surface area, abundant functional groups and low cost, biochar is
widely used for pollutant removal. The adsorption performance of biochar is related to …

We devise deep nearest centroids (DNC), a conceptually elegant yet surprisingly effective
network for large-scale visual recognition, by revisiting Nearest Centroids, one of the most …

Deep neural networks achieve remarkable performances on a wide range of tasks with the
aid of large-scale labeled datasets. Yet these datasets are time-consuming and labor …

Medical image segmentation plays an irreplaceable role in computer-assisted diagnosis,
treatment planning and following-up. Collecting and annotating a large-scale dataset is …

Pre-trained code language models have achieved promising performance in code
generation and improved the programming efficiency of human developers. However, their …

Learning with noisy labels (LNL) aims to ensure model generalization given a label-
corrupted training set. In this work, we investigate a rarely studied scenario of LNL on fine …

Abstract Language models (LMs) trained on vast quantities of unlabelled data have greatly
advanced the field of natural language processing (NLP). In this study, we re-visit the widely …

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water
resources management, hydrological processes, and agricultural production. The FAO-56 …

Semi-supervised text classification (SSTC) aims at text classification with few labeled data
and massive unlabeled data. Recent works achieve this task by pseudo-labeling methods …

Deep learning models have demonstrated remarkable success in multi-organ segmentation
but typically require large-scale datasets with all organs of interest annotated. However …