Utilizing mutual learning can effectively enhance the performance of peer BNNs.

Automatically generating novel and interesting games is a complex task.

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs.

BACON allows computational resources to be focused on simulations that provide the most value in terms of reducing epistemic uncertainty.

Recent work has shown that CNN-based depth and ego-motion estimators can be learned using unlabelled monocular videos. However, the performance is limited by unidentified moving objects that violate the underlying static scene assumption in geometric image reconstruction.

Pedestrian pre-collision pose is one of the key factors to determine the degree of pedestrian-vehicle injury in collision. Human pose estimation algorithm is an effective method to estimate pedestrian emergency pose from accident video.