An important factor to guarantee a fair use of data-driven recommendation systems is that we should be able to communicate their uncertainty to decision makers.
We demonstrate that this representation is particularly amenable to learning descriptors with deep networks.
In this paper, we propose a simple feed-forward deep network for motion prediction, which takes into account both temporal smoothness and spatial dependencies among human body joints.
Recent advances in deep learning, especially the discovery of various attention mechanisms and newer architectures in addition to widely used RNN and CNN in natural language processing, have allowed us to make better use of the temporal ordering of items that each user has engaged with.
We claim the following characteristics for a versatile environment model: accuracy, applicability, usability, and scalability.
In this work we develop a distributed least squares approximation (DLSA) method, which is able to solve a large family of regression problems (e. g., linear regression, logistic regression, Cox's model) on a distributed system.
This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT.