A Deep Learning Architecture for Sentence Induction


A Deep Learning Architecture for Sentence Induction – Sentence Induction (sentence) aims to predict the outcome of an input text by predicting the next word. We propose a novel framework that consists of two parts: learning sequence invariant recurrent neural networks (RNNs) and recurrent recurrent neural models; and learning a class of recurrent recurrent neural networks (RNNs). In this paper, we demonstrate the effectiveness of both methods, on two challenging data sets: (i) semantic segmentation and (ii) translation from English to Chinese. The proposed model, that is trained with only the feature representations of the input text, successfully predicts the outcome and correctly identifies an object, which was added to the sentence. To validate our learned RNN system, we train it in three different environments, and tested it on two tasks: prediction of a single sentence and translation from Chinese to English.

This paper presents a novel method to automatically generate abstract images from high resolution images. The extracted scene models, for each scene, are constructed using sparse, sparse representations of images and high resolution images. For each image, the images are decomposed into a set of sparse representations by using a supervised prior learning algorithm. As images are compact and densely sampled, these sparse representations are a proxy for sparse representation of the data. The extraction of the image representations is achieved using a deep convolutional network (CNN) with a small number of labeled images for each scene model. The CNN composes the sparse representations and extracts their semantic information from the images. The extracted semantic features from the scene are used to guide the CNN in terms of predicting the semantic representation and classification accuracy. The extracted semantic features are then used in the prediction task. The final classification results are compared to the state-level prediction task. Experiments show promising performance as compared to human performance.

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A Deep Learning Architecture for Sentence Induction

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  • A Novel Face Alignment Based on Local Contrast and Local Hue

    Deep Learning for Large-Scale Video Annotation: A SurveyThis paper presents a novel method to automatically generate abstract images from high resolution images. The extracted scene models, for each scene, are constructed using sparse, sparse representations of images and high resolution images. For each image, the images are decomposed into a set of sparse representations by using a supervised prior learning algorithm. As images are compact and densely sampled, these sparse representations are a proxy for sparse representation of the data. The extraction of the image representations is achieved using a deep convolutional network (CNN) with a small number of labeled images for each scene model. The CNN composes the sparse representations and extracts their semantic information from the images. The extracted semantic features from the scene are used to guide the CNN in terms of predicting the semantic representation and classification accuracy. The extracted semantic features are then used in the prediction task. The final classification results are compared to the state-level prediction task. Experiments show promising performance as compared to human performance.


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