Researchers are defining new types of interactions between humans and machine learning
algorithms generically called human-in-the-loop machine learning. Depending on who is in …

Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

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 …

Abstract Design of de novo biological sequences with desired properties, like protein and
DNA sequences, often involves an active loop with several rounds of molecule ideation and …

The notion of uncertainty is of major importance in machine learning and constitutes a key
element of machine learning methodology. In line with the statistical tradition, uncertainty …

Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

Hyperspectral imaging (HSI) has been extensively utilized in many real-life applications
because it benefits from the detailed spectral information contained in each pixel. Notably …

In this work, we provide a survey of active learning (AL) for its applications in natural
language processing (NLP). In addition to a fine-grained categorization of query strategies …

Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

While deep learning (DL) is data-hungry and usually relies on extensive labeled data to
deliver good performance, Active Learning (AL) reduces labeling costs by selecting a small …