Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity.
Scholars have increasingly used a combination of geographical information science (GIS) …

Camera pose estimation in known scenes is a 3D geometry task recently tackled by multiple
learning algorithms. Many regress precise geometric quantities, like poses or 3D points …

In this paper, we propose a balancing training method to address problems in imbalanced
data learning. To this end, we derive a new loss used in the balancing training phase that …

Data normalization is one of the pre-processing approaches where the data is either scaled
or transformed to make an equal contribution of each feature. The success of machine …

The electroencephalogram (EEG) provides a non-invasive, minimally restrictive, and
relatively low-cost measure of mesoscale brain dynamics with high temporal resolution …

Machine-learning models have demonstrated great success in learning complex patterns
that enable them to make predictions about unobserved data. In addition to using models for …

Finding local features that are repeatable across multiple views is a cornerstone of sparse
3D reconstruction. The classical image matching paradigm detects keypoints per-image …

The goal of data attribution is to trace model predictions back to training data. Despite a long
line of work towards this goal, existing approaches to data attribution tend to force users to …

As machine learning systems grow in scale, so do their training data requirements, forcing
practitioners to automate and outsource the curation of training data in order to achieve state …