Active learning (AL) attempts to maximize a model's performance gain while annotating the
fewest samples possible. Deep learning (DL) is greedy for data and requires a large amount …

Which samples should be labelled in a large dataset is one of the most important problems
for the training of deep learning. So far, a variety of active sample selection strategies related …

Reward learning enables the application of reinforcement learning (RL) to tasks where
reward is defined by human judgment, building a model of reward by asking humans …

Common acquisition functions for active learning use either uncertainty or diversity
sampling, aiming to select difficult and diverse data points from the pool of unlabeled data …

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 …

The great success of modern machine learning models on large datasets is contingent on
extensive computational resources with high financial and environmental costs. One way to …

Recent work in natural language processing (NLP) has yielded appealing results from
scaling model parameters and training data; however, using only scale to improve …

The ability to train complex and highly effective models often requires an abundance of
training data, which can easily become a bottleneck in cost, time, and computational …

The promise of active learning (AL) is to reduce labelling costs by selecting the most
valuable examples to annotate from a pool of unlabelled data. Identifying these examples is …

Large scale machine learning and deep models are extremely data-hungry. Unfortunately,
obtaining large amounts of labeled data is expensive, and training state-of-the-art models …