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Deep learning for autonomous driving

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Convolutional Neural Network (CNN) has a great potential and can be applied to various tasks by changing the relationships between inputs and outputs. Autonomous driving is one of the hot topics among them. In this talk, we will introduce our research using CNNs for tasks such as object detection, semantic segmentation and human pose estimation necessary for realizing automatic driving. We will also introduce the reinforcement learning of driving behavior into simulation.

This talk is part of the Machine Learning @ CUED series.

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