Reconstructing the Autonomous Driving Problem from a Single Image – We present a new methodology for the task of automatic driving prediction. Our method is based on convolutional networks, a highly useful class of neural networks for prediction. We show that the best prediction results are obtained from a single image taken with different cameras. In this context, we study several scenarios in the car and learn a novel network structure, called a self-organized multi-modality network (SMN). We then demonstrate that the SMN can be used to predict and learn to drive accurately from a single image taken without the need for a camera and video. By learning a set of parameters, we can then use the SMN to solve an online learning problem with a large training set in each of the three settings. The SMN learned from its image is then used as a proxy to predict the next one. Our method shows competitive performance when all the parameters are well-aligned and the simulator can be easily deployed to the road. To evaluate our method, we evaluate the performance of our method in comparison with previous state-of-the-art machine learning methods.

Randomization is generally regarded as a problem of finding an optimal policy that optimizes the information for a given policy. In this paper, we explore how randomized policy optimization can be performed by minimizing the cost function of an unknown policy in terms of the objective function itself, under the assumption that the policy optimizes in the expected (or the unobserved) direction. The expected cost function itself can provide an information-theoretic explanation for this knowledge-theoretic assumption, and thus provides a framework and empirical results for estimating cost functions for unknown policy optimization problems.

Adversarial Data Analysis in Multi-label Classification

A Deep Learning Method for Optimal Vehicle Location

# Reconstructing the Autonomous Driving Problem from a Single Image

Unsupervised Learning with Randomized LabelingsRandomization is generally regarded as a problem of finding an optimal policy that optimizes the information for a given policy. In this paper, we explore how randomized policy optimization can be performed by minimizing the cost function of an unknown policy in terms of the objective function itself, under the assumption that the policy optimizes in the expected (or the unobserved) direction. The expected cost function itself can provide an information-theoretic explanation for this knowledge-theoretic assumption, and thus provides a framework and empirical results for estimating cost functions for unknown policy optimization problems.