Andrew Y. Ng

TL;DR: An approach in which supervised learning is first used to estimate depths from single monocular images, which is able to learn monocular vision cues that accurately estimate the relative depths of obstacles in a scene is presented.

TL;DR: Deep Voice lays the groundwork for truly end-to-end neural speech synthesis and shows that inference with the system can be performed faster than real time and describes optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x speedups over existing implementations.

TL;DR: This paper presents a new algorithm that, given only a generative model (a natural and common type of simulator) for an arbitrary MDP, performs on-line, near-optimal planning with a per-state running time that has no dependence on the number of states.

TL;DR: The answer is that certain convolutional pooling architectures can be inherently frequency selective and translation invariant, even with random weights, and the viability of extremely fast architecture search is demonstrated by using random weights to evaluate candidate architectures, thereby sidestepping the time-consuming learning process.

TL;DR: This work addresses the problem of segmenting 3D scan data into objects or object classes by using a recently proposed maximum-margin framework to discriminatively train the model from a set of labeled scans and automatically learn the relative importance of the features for the segmentation task.