Video Description and Action Recognition – Most of the popular methods for face recognition are based on word embeddings. This paper develops a language learning framework for word embeddings. We propose to encode the input as a set of binary word vectors, and extract the encoded language with some probability function on the binary vectors. To build a language learning system we propose to extract a set of binary vector representations. A novel approach is to encode word vectors by learning a word embedding function. Our approach uses a word vector to encode words, and a word vector to encode semantic phrases. We show that an embedding function for word representations can be learned to build a language learning system with good performance. We further develop a novel neural network architecture to learn the word vectors. Experimental results on the PASCAL VOC dataset demonstrate that our proposed language learning framework outperforms the other standard methods.

A major problem in statistical learning methods is to learn a mixture of two groups of data. We propose a hybrid framework for modeling the mixture of both groups of data and propose to model them independently on their variance. Our framework uses a Bayesian metric for the unknown variable, which can be seen as a surrogate for the variance of the mixture. Given the covariance matrix, we use an inference strategy using the linear kernel to approximate the expected distribution of the observed covariance matrix and a logistic regression method, which can be used to build a model. The model is then transformed to a nonparametric mixture and the parameters are learned as the covariance matrix. We have designed the framework using a novel algorithm based on variational inference to learn the parameters. Experimental evaluation results show that the framework is very efficient, outperforming state-of-the-art approaches (such as Viterbi et al). The framework is also scalable with a reasonable performance.

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Stochastic Regularized Gradient Methods for Deep Learning

# Video Description and Action Recognition

Probabilistic Models for Estimating Multiple Risk Factors for a Group of Patients

Multi-modality Deep Learning with Variational Hidden-Markov Models for ClassificationA major problem in statistical learning methods is to learn a mixture of two groups of data. We propose a hybrid framework for modeling the mixture of both groups of data and propose to model them independently on their variance. Our framework uses a Bayesian metric for the unknown variable, which can be seen as a surrogate for the variance of the mixture. Given the covariance matrix, we use an inference strategy using the linear kernel to approximate the expected distribution of the observed covariance matrix and a logistic regression method, which can be used to build a model. The model is then transformed to a nonparametric mixture and the parameters are learned as the covariance matrix. We have designed the framework using a novel algorithm based on variational inference to learn the parameters. Experimental evaluation results show that the framework is very efficient, outperforming state-of-the-art approaches (such as Viterbi et al). The framework is also scalable with a reasonable performance.

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