Real world scenarios present a challenge for text classification, since labels are usually
expensive and the data is often characterized by class imbalance. Active Learning (AL) is a …

A recent trend in artificial intelligence is the use of pretrained models for language and
vision tasks, which have achieved extraordinary performance but also puzzling failures …

The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …

Several recent papers investigate Active Learning (AL) for mitigating the data dependence
of deep learning for natural language processing. However, the applicability of AL to real …

Active learning is a powerful tool when labelling data is expensive, but it introduces a bias
because the training data no longer follows the population distribution. We formalize this …

Partial label learning aims to induce a multi-class classifier from training examples where
each of them is associated with a set of candidate labels, among which only one is the …

Active learning increases the effectiveness of labeling when only subsets of unlabeled
datasets can be processed manually. To our knowledge, existing algorithms are designed …

Pool-based active learning (AL) aims to optimize the annotation process (ie, labeling) as the
acquisition of annotations is often time-consuming and therefore expensive. For this …

We address the task of active learning in the context of semantic segmentation and show
that self-consistency can be a powerful source of self-supervision to greatly improve the …

Many real-world ML deployments face the challenge of training a rare category model with a
small labeling bud-get. In these settings, there is often access to large amounts of unlabeled …