Remarkably, the solutions learned for each individual task resemble those obtained by solving a kernel regression problem, revealing a novel connection between neural networks and kernel methods.

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

To begin investing in measuring the relevance of a conversation to a predefined agenda topic, a comprehensive dataset of conversations associated with the topic of each conversation section is needed.

Effective communication requires the ability to refer to specific parts of an observation in relation to others.

Modern machine learning systems are able to learn extremely complicated relations from data.

T o this end, we propose a simple yet powerful paradigm for seamlessly unifying different human pose and shape-related tasks and datasets. Our formulation is centered on the ability - both at training and test time - to query any arbitrary point of the human volume, and obtain its estimated location in 3D. We achieve this by learning a continuous neural field of body point localizer functions, each of which is a differently parameterized 3D heatmap-based convolutional point localizer (detector).