An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents

An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents – Training Convolutional Neural Networks (CNNs) on large-scale, unlabeled data was considered a key challenge due to the difficulty in training discriminative models. In this paper, we provide a generalization of the standard CNN approach of inferring labels from unlabeled data. We propose a novel technique for a non-convex optimization problem where the objective is to optimize the training data by solving a discrete, non-convex, problem. Our approach shows promising theoretical results.

We propose an extension of the standard Active Contour Model (ACM) for tracking, where the target point is the target of a visual tracking system as well as a background object. The objective of the ACM is to provide a better and more accurate tracking of objects in an environment. In particular, the ACM is based on the notion of a stationary target and the target is the foreground. A non-stationary object is a part of the environment and a foreground object is a part of the background. These two concepts are also a necessary ingredient in the ACM. Furthermore, the ACM can also be regarded as a semantic tracking method which allows a user to track objects in the environment. We show that our method has the same performance as the ACM, with the goal of making the user aware of the objects in the environment. A comprehensive evaluation on three well-known challenging real-world environment tracks shows the effectiveness of our approach.

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An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning Agents

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  • A note on the Lasso-dependent Latent Variable Model

    A Novel Passive Contour Model for Visual TrackingWe propose an extension of the standard Active Contour Model (ACM) for tracking, where the target point is the target of a visual tracking system as well as a background object. The objective of the ACM is to provide a better and more accurate tracking of objects in an environment. In particular, the ACM is based on the notion of a stationary target and the target is the foreground. A non-stationary object is a part of the environment and a foreground object is a part of the background. These two concepts are also a necessary ingredient in the ACM. Furthermore, the ACM can also be regarded as a semantic tracking method which allows a user to track objects in the environment. We show that our method has the same performance as the ACM, with the goal of making the user aware of the objects in the environment. A comprehensive evaluation on three well-known challenging real-world environment tracks shows the effectiveness of our approach.


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