Fast and Accurate Stochastic Variational Inference


Fast and Accurate Stochastic Variational Inference – We explore the topic of statistical learning in the context of Bayesian networks. We explore the use of latent space to model the structure (in terms of features) of data sets by performing Bayesian inference in the latent space. We show that a simple model such as Bayesian network is capable of learning much more informative information about data than a general random process of a priori knowledge, and our experiments on synthetic data show that even a priori and probabilistic knowledge can be learned by the latent model. We finally show that learning Bayesian network representations from data sets is challenging, since each hidden variable is not its neighbors, and therefore the latent space has to be adapted to learn useful information. This is especially true in environments with high noise and computational overhead.

The multi-camera systems have proven successful in many challenging aspects of the visual inspection process such as: the task of detecting objects and objects’ poses in images; the task of identifying missing items in images; and the task of detecting objects that look like objects when being examined. However, due to their multiple nature of the images, each camera is different and therefore different camera models with different functionality can have different abilities and they may have different performance characteristics. In this paper, we propose a novel method for automatically recognizing objects and objects at different positions, scale and orientation in images and videos from a single camera. The concept is to automatically make use of the camera views and attributes to extract the most relevant information from the images. To this end, we use a visual segmentation based approach that takes a series of large-scale and real-time camera views to extract various object recognition features, using a spatial and spatial-temporal framework. In experiments, the proposed method is competitive with state-of-the-art object detection methods on PASCAL VOC benchmark datasets.

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Fast and Accurate Stochastic Variational Inference

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  • A New Algorithm for Optimizing Discrete Energy Minimization

    Automatic Image Aesthetic Assessment Based on Deep Structured AttentionsThe multi-camera systems have proven successful in many challenging aspects of the visual inspection process such as: the task of detecting objects and objects’ poses in images; the task of identifying missing items in images; and the task of detecting objects that look like objects when being examined. However, due to their multiple nature of the images, each camera is different and therefore different camera models with different functionality can have different abilities and they may have different performance characteristics. In this paper, we propose a novel method for automatically recognizing objects and objects at different positions, scale and orientation in images and videos from a single camera. The concept is to automatically make use of the camera views and attributes to extract the most relevant information from the images. To this end, we use a visual segmentation based approach that takes a series of large-scale and real-time camera views to extract various object recognition features, using a spatial and spatial-temporal framework. In experiments, the proposed method is competitive with state-of-the-art object detection methods on PASCAL VOC benchmark datasets.


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