Stereoscopic 2D: Semantics, Representation and Rendering

Stereoscopic 2D: Semantics, Representation and Rendering – We consider a novel problem: how to find a segmentation that best matches a given dataset given any data points? We propose a general learning algorithm. Our algorithm relies on the observation that most of the dataset is labeled and a large number of samples are missing. To alleviate the problem of missing data and of overfitting, we propose an efficient algorithm to simultaneously classify and reuse the labels of the labeled data. We show that our algorithm performs well in scenarios where the label space is sufficiently large, particularly for the most difficult cases. We also compare our algorithm to recent state-of-the-art deep learning models, including both synthetic and real data, on several benchmark datasets.

We present a new approach to a recurrent neural network (RNN) architecture for action detection. Our approach employs a weighted convolutional layer to capture the action content in a long time scale in a supervised learning setting. After a supervised learning task, a set of training examples are collected from the training dataset in which the action content is detected. We then iteratively train multiple layers to extract the action content that is relevant and then utilize the input from the previous layers for the next task. Our method achieves state of the art performance in both tasks; with a maximum of 6.8% training rate for PCC-200, the performance rate of this network is 1.37 times higher than that of the existing state of the art neural networks (i.e., RNNs and ImageNet). The experimental results of our method indicate that we can achieve state of the art performance.

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Stereoscopic 2D: Semantics, Representation and Rendering

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  • Machine Learning for Human Identification

    Fast Reinforcement Learning in Density Estimation with Recurrent Neural NetworksWe present a new approach to a recurrent neural network (RNN) architecture for action detection. Our approach employs a weighted convolutional layer to capture the action content in a long time scale in a supervised learning setting. After a supervised learning task, a set of training examples are collected from the training dataset in which the action content is detected. We then iteratively train multiple layers to extract the action content that is relevant and then utilize the input from the previous layers for the next task. Our method achieves state of the art performance in both tasks; with a maximum of 6.8% training rate for PCC-200, the performance rate of this network is 1.37 times higher than that of the existing state of the art neural networks (i.e., RNNs and ImageNet). The experimental results of our method indicate that we can achieve state of the art performance.


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