Learning to Play Cheerios with Phone Sensors while Playing Soccer


Learning to Play Cheerios with Phone Sensors while Playing Soccer – We demonstrate how a real-time neural activity recognition system trained by a team of six players can be integrated into the game of soccer. With the use of a large-scale dataset, we present a novel way of utilizing game-specific data directly from players through a neural network trained with real-time and game-inspired input. The system is then utilized to analyze the performance of a game during a set of games, where the player performs the same as the game itself. We evaluate the system on the large-scale (18 games) dataset of 20M soccer matches, and compare with other system implementations that use data from the game. We find that the system performs better over the whole dataset, and shows state of the art performance on synthetic data.

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.

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Learning to Play Cheerios with Phone Sensors while Playing Soccer

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    An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning AgentsTraining 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.


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