Optimizing for humans' latent preferences remains a grand challenge in route recommendation.
no code implementations • 4 Feb 2022 • Luyang Liu, David Racz, Kara Vaillancourt, Julie Michelman, Matt Barnes, Stefan Mellem, Paul Eastham, Bradley Green, Charles Armstrong, Rishi Bal, Shawn O'Banion, Feng Guo
Hard-braking events have been widely used as a safety surrogate due to their relatively high prevalence and ease of detection with embedded vehicle sensors.
In this work, we examine a novel forecasting approach for COVID-19 case prediction that uses Graph Neural Networks and mobility data.
In many environments, only a relatively small subset of the complete state space is necessary in order to accomplish a given task.
A successful robot-assisted feeding system requires bite acquisition of a wide variety of food items.
We show that the state-of-the-art methods such as GAIL and behavior cloning, due to their choice of loss function, often incorrectly interpolate between such modes.
One significant challenge to scaling entity resolution algorithms to massive datasets is understanding how performance changes after moving beyond the realm of small, manually labeled reference datasets.