Reinforcement learning and imitation learning have seen success in many domains, including autonomous helicopter flight, Atari, simulated locomotion, Go, robotic manipulation. However, sample complexity of these methods ...

Traditional sensor data can be augmented with new data sources such as roadmaps and geographical information system (GIS) Lidar/video to offer emerging unmanned aircraft systems (UAS) and urban air mobility (UAM) a new ...

Deep learning has enabled rapid progress in diverse problems in vision, speech, healthcare, and beyond. This progress has been driven by breakthroughs in algorithms that can harness massive datasets and powerful compute ...

Machine perception is a key step toward artificial intelligence in domains such as self-driving cars, industrial automation, and robotics. Much progress has been made in the past decade, driven by machine learning, ...

Generative Adversarial Nets (GANs) is a framework for training deep generative models, due to Goodfellow et al'13. It involves a competition between a generator net that tries to produce realistic images, and a discriminator ...

We will present some recent work on matrix factorizations with applications that range from full-azimuth seismic data processing w/ coil acquisition to seismic data compression & recovery w/ on-the-fly data extraction, and ...

Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, ...

In this talk, we will cover general info about Samsung Research America and more specifically the research and projects happening within the Artificial Intelligence team including personal assistants, dialogue systems, ...

We introduce and analyze a new technique for model reduction in deep neural networks. Our algorithm prunes (sparsifies) a trained network layer-wise, removing connections at each layer by addressing a convex problem. We ...

The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that are learned from large datasets. In this ...