Andrew Y. Ng

TL;DR: It is shown that neural networks originally designed for image recognition can be trained to detect objects within images, regardless of their class, including objects for which no bounding box labels have been provided.

TL;DR: ML improved risk adjustment models and the incorporation of social determinants of health (SDH) indicators reduced underpayment in several vulnerable populations.

TL;DR: The first Agriculture-Vision Challenge as mentioned in this paper aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with the challenge dataset.

TL;DR: In this paper, a selection input is received for a candidate location in a video frame of the video, and the candidate location is traced in subsequent video frames by approximating three-dimensional camera motion between two frames using a model that compensates for camera rotations, camera translations and zooming.

TL;DR: This work presents a class of approximate inference algorithms for graphical models of the QMR-DT type, and gives convergence rates for these algorithms and for the Jaakkola and Jordan (1999) algorithm, and verifies theoretical predictions empirically.