An Event Core of Deep Belief Networks for Multi-Person Perception in Navigation


An Event Core of Deep Belief Networks for Multi-Person Perception in Navigation – In this paper, we present a new approach for the task of object detection in natural scenes. This is an end-to-end learning algorithm which learns from scene data, i.e., the scene data is not dense enough to be used for object detection. We propose a novel algorithm which considers different aspects of the scene data, e.g., scene content, and then trains a Convolutional Neural Network (CNN) to learn a scene content representation. We test our method on several video datasets of different types, and demonstrate that our method achieves promising results on the challenging task of object detection in the natural world. As a result, by comparing the results of different CNN variants, we can improve our proposed method.

This paper presents a probabilistic model for online learning with spatio-temporal information. The model proposes a learning algorithm that combines a novel learning algorithm with a temporal learning algorithm and a stochastic coupling between the sparse vector and the neighborhood lattice. This model does not require an extra parameter to obtain the posterior distribution, which makes solving it much easier. Our approach obtains both an efficient and competitive inference algorithm: (1) our algorithm is evaluated on synthetic data and (2) the algorithm is evaluated in real data with a non-parametric covariance matrix.

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An Event Core of Deep Belief Networks for Multi-Person Perception in Navigation

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  • Robust Multi-Labeling Convolutional Neural Networks for Driver Activity Tagged Videos

    An efficient model with a stochastic coupling between the sparse vector and the neighborhood latticeThis paper presents a probabilistic model for online learning with spatio-temporal information. The model proposes a learning algorithm that combines a novel learning algorithm with a temporal learning algorithm and a stochastic coupling between the sparse vector and the neighborhood lattice. This model does not require an extra parameter to obtain the posterior distribution, which makes solving it much easier. Our approach obtains both an efficient and competitive inference algorithm: (1) our algorithm is evaluated on synthetic data and (2) the algorithm is evaluated in real data with a non-parametric covariance matrix.


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