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 …

Fully automatic deep learning has become the state-of-the-art technique for many tasks
including image acquisition, analysis and interpretation, and for the extraction of clinically …

Many recent approaches to natural language tasks are built on the remarkable abilities of
large language models. Large language models can perform in-context learning, where they …

This article discusses the pitfalls and opportunities of AI in marketing through the lenses of
knowledge creation and knowledge transfer. First, we discuss the notion of “higher-order …

Convolutional neural network-based approaches have achieved remarkable progress in
semantic segmentation. However, these approaches heavily rely on annotated data which …

The performance of deep neural networks improves with more annotated data. The problem
is that the budget for annotation is limited. One solution to this is active learning, where a …

We develop BatchBALD, a tractable approximation to the mutual information between a
batch of points and model parameters, which we use as an acquisition function to select …

Active learning aims to develop label-efficient algorithms by sampling the most
representative queries to be labeled by an oracle. We describe a pool-based semi …

Supervised learning aims to build a function or model that seeks as many mappings as
possible between the training data and outputs, where each training data will predict as a …

Deep learning methods have become the de-facto standard for challenging image
processing tasks such as image classification. One major hurdle of deep learning …