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 …

Machine learning is being increasingly used by individuals, research institutions, and
corporations. This has resulted in the surge of Machine Learning-as-a-Service (MLaaS) …

There is an increasing need for effective active learning algorithms that are compatible with
deep neural networks. This paper motivates and revisits a classic, Fisher-based active …

Deep learning has achieved widespread success in medical image analysis, leading to an
increasing demand for large-scale expert-annotated medical image datasets. Yet, the high …

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised
machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data …

We study active sampling algorithms for linear regression, which aim to query only a small
number of entries of a target vector and output a near minimizer to the objective function. For …

Image pattern recognition in the field of big data has gained increasing importance and
attention from researchers and practitioners in many domains of science and technology …

Abstract We propose Active Surrogate Estimators (ASEs), a new method for label-efficient
model evaluation. Evaluating model performance is a challenging and important problem …

Deep convolutional neural networks (CNN) have recently achieved superior performance at
the task of medical image segmentation compared to classic models. However, training a …

The experimental design problem concerns the selection of k points from a potentially large
design pool of p-dimensional vectors, so as to maximize the statistical efficiency regressed …