Video games are not all that simple


Video games are not all that simple – We present a novel approach for learning game theory from data. Our solution is an approximation approach, in which we show that when a system is made up of random variables, the information contained in the initial data is likely to be biased by its distribution and this biased information does not influence the learning process. Our approach is also robust with respect to the noise in the initial distribution, which can be seen as a result of the initial distribution’s non-random behavior.

In this paper, we propose a new neural network based system for the purpose of unsupervised multi-task classification based on visual-spatial descriptors. The proposed system is a convolutional neural network (CNN) capable of performing well in the task of multi-task classification. The system is trained using a CNN-like architecture and an end-to-end network architecture. The CNN architecture is composed of two subnetworks: one is a single convolutional network that trains the discriminators on top of a convolutional neural network, the other is a distributed module that produces the discriminators on the end. When the CNN architecture is trained, the discriminators of the learned CNN are classified into the task of each individual category. The network is trained on a large dataset of images acquired from the road to demonstrate its effectiveness in unsupervised detection of pedestrians in an urban environment. By using the CNN architecture, the system outperforms the state-of-the-art performance compared to other existing state of the art CNNs on the PASCAL VOC 2015 dataset.

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Video games are not all that simple

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  • Robust Online Learning: A Nonparametric Eigenvector Approach

    A Multi-Task Approach to Unsupervised Mobile Vehicle Detection and Localization Using Visual Cues from Social MediaIn this paper, we propose a new neural network based system for the purpose of unsupervised multi-task classification based on visual-spatial descriptors. The proposed system is a convolutional neural network (CNN) capable of performing well in the task of multi-task classification. The system is trained using a CNN-like architecture and an end-to-end network architecture. The CNN architecture is composed of two subnetworks: one is a single convolutional network that trains the discriminators on top of a convolutional neural network, the other is a distributed module that produces the discriminators on the end. When the CNN architecture is trained, the discriminators of the learned CNN are classified into the task of each individual category. The network is trained on a large dataset of images acquired from the road to demonstrate its effectiveness in unsupervised detection of pedestrians in an urban environment. By using the CNN architecture, the system outperforms the state-of-the-art performance compared to other existing state of the art CNNs on the PASCAL VOC 2015 dataset.


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