As deep learning models are applied to increasingly diverse problems, a key bottleneck is
gathering enough high-quality training labels tailored to each task. Users therefore turn to …

Electrophysiological signals exhibit both periodic and aperiodic properties. Periodic oscillations
have been linked to numerous physiological, cognitive, behavioral and disease states. …

Training accurate classifiers requires many labels, but each label provides only limited
information (one bit for binary classification). In this work, we propose BabbleLabble, a framework …

Visual knowledge bases such as Visual Genome power numerous applications in computer
vision, including visual question answering and captioning, but suffer from sparse, …

Obtaining enough labeled data to robustly train complex discriminative models is a major
bottleneck in the machine learning pipeline. A popular solution is combining multiple sources …

Electrophysiological signals across species and recording scales exhibit both periodic and
aperiodic features. Periodic oscillations have been widely studied and linked to numerous …

Biomedical repositories such as the UK Biobank provide increasing access to prospectively
collected cardiac imaging, however these data are unlabeled, which creates barriers to their …

Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent
weak supervision approaches combine labels from multiple noisy sources by estimating their …

Since manually labeling training data is slow and expensive, recent industrial and scientific
research efforts have turned to weaker or noisier forms of supervision sources. However, …

We propose a new algorithm for recovering both complex field (phase and amplitude) and
source distribution (illumination spatial coherence) from a stack of intensity images captured …