Learning the Genre Vectors Using Word Embedding


Learning the Genre Vectors Using Word Embedding – Generation of new words has tremendous impact on human comprehension, thus, learning about them is essential to better understand the information that comes from them. Previous work has focused on word embeddings on the same task as word embedding on the task of word similarity in a language. By using a word embedding dataset of English Wikipedia articles, we demonstrate the effectiveness of learning words as embeddings in a language with more than 1 million words. We also show how to improve a word embedding dataset by increasing the size of the embedding space.

In this paper we present a framework for image-recognition based on the use of semantic content. The key idea is to compute a 3D transformation of the face image for each frame and learn a joint probability graph that maps to the same 3D data structure. The framework is simple to implement but the main idea is to learn the joint probability graph by using three state-of-the-art deep neural networks (DNNs) in conjunction with a CNN, and the framework is then implemented using deep convolutional layers. We evaluate four DNNs and three LSTMs to classify each frame, and train two CNN-based models on two datasets with different resolutions and different pose. We observe that both CNN and LSTM can be utilized effectively to achieve high classification rates and that they achieve the same rate of classification compared with state-of-the-art CNNs and LSTMs.

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Learning the Genre Vectors Using Word Embedding

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  • A Kernel Regression Approach to Multi-Resolution Multi-Task Learning

    A Comprehensive Toolkit for Deep Face RecognitionIn this paper we present a framework for image-recognition based on the use of semantic content. The key idea is to compute a 3D transformation of the face image for each frame and learn a joint probability graph that maps to the same 3D data structure. The framework is simple to implement but the main idea is to learn the joint probability graph by using three state-of-the-art deep neural networks (DNNs) in conjunction with a CNN, and the framework is then implemented using deep convolutional layers. We evaluate four DNNs and three LSTMs to classify each frame, and train two CNN-based models on two datasets with different resolutions and different pose. We observe that both CNN and LSTM can be utilized effectively to achieve high classification rates and that they achieve the same rate of classification compared with state-of-the-art CNNs and LSTMs.


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