Probabilistic Models for Time-Varying Probabilistic Inference

Probabilistic Models for Time-Varying Probabilistic Inference – We present a machine learning approach to image classification, which utilizes sparse representations for classification tasks. We build on the recent success of unsupervised learning for deep learning, where supervised learning is used to automatically infer an image from some labels. Despite the huge successes of unsupervised training on a large range of datasets, sparse representation learning has not yet achieved its potential. Here, we present a novel sparse representation learning method called sparse-LSTM for classification tasks. Our method is inspired by the notion of posterior probability density. However, the posterior density is defined as the difference between a sum of multiple likelihood functions, thus requiring a more complex parameterization than the model is capable of doing. In addition to their simplicity, our method has the virtue of being computationally efficient to perform on large networks. We evaluated our method on synthetic and real datasets, and show that it outperforms the state-of-the-art on both tasks. We also demonstrate that sparse representations are an effective generalization of the common deep learning framework, suggesting that sparse representations are very useful for practical deep learning applications.

We present an algorithm that can extract 3D images based on depth maps, such that the pixel classifier can more accurately detect the full image. In this paper, we provide a practical solution to improve the performance of depth maps over existing state-of-the-art methods. Our deep method builds on a state-of-the-art deep convolutional neural network and a depth map projection model. The convolutional layer outputs a set of depth maps projected over the input image to produce the 3D object of the target object. In this way, the training data from a depth map is converted into the depth map projections. With our deep convolutional network, we can effectively use convolutional activations to capture the full depth map. Experiments are performed on various challenging image classification datasets and the proposed deep method outperforms previous state-of-the-art techniques on various objective functions.

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An Open Source Framework for Video Processing from Natural Scene Data

Probabilistic Models for Time-Varying Probabilistic Inference

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  • A Hierarchical Latent Model for Learning Distribution Regression

    Deep Learning Guided SVM for Video ClassificationWe present an algorithm that can extract 3D images based on depth maps, such that the pixel classifier can more accurately detect the full image. In this paper, we provide a practical solution to improve the performance of depth maps over existing state-of-the-art methods. Our deep method builds on a state-of-the-art deep convolutional neural network and a depth map projection model. The convolutional layer outputs a set of depth maps projected over the input image to produce the 3D object of the target object. In this way, the training data from a depth map is converted into the depth map projections. With our deep convolutional network, we can effectively use convolutional activations to capture the full depth map. Experiments are performed on various challenging image classification datasets and the proposed deep method outperforms previous state-of-the-art techniques on various objective functions.


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