Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxel-based Deep Learning

Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxel-based Deep Learning – Many computer vision tasks require large, dense data, with most approaches either using structured models or using linear models. In this work we propose a novel framework for Deep Learning that supports real-time inference of models over deep networks and networks that are trained on data to learn to interactively model them. We demonstrate that this framework is effective, and achieves encouraging improvements over supervised learning on a number of challenging models.

This paper concerns the problem of learning nonparametric models based on a non-convex, orthogonal embedding. This embedding allows us to address both optimization and learning problems. Our approach relies on a priori knowledge about the embedding space, which is necessary for efficient optimization. We show that by considering the embedding space in terms of the dimensionality of the data rather than the model weight (and dimensionality reduction), we can improve the performance of many existing nonparametric learning methods. We discuss the performance of our approach on two general nonparametric learning problems: the classification problem and the regression problem.

This paper presents an end-to-end approach for the semantic parsing of human language using machine learning based tools. The proposed approach is composed of two steps: (i) it learns semantic relationships by learning the representations learned by the human experts and (ii) it provides a natural way for semantic parsers to interact with the human experts. The proposed end-to-end approach is evaluated and compared with a previous state-of-the-art baseline based on Semantic Pattern Recognition (SPR) tasks. The results show that our approach outperforms the other baseline models compared to human experts without sacrificing performance by a large degree.

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Learning Deep Structured Models by Fully Convolutional Neural Networks Using Supervoxel-based Deep Learning

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  • Generating Multi-View Semantic Parsing Rules for Code-Switching

    Generating Semantic Representations using Greedy MethodsThis paper presents an end-to-end approach for the semantic parsing of human language using machine learning based tools. The proposed approach is composed of two steps: (i) it learns semantic relationships by learning the representations learned by the human experts and (ii) it provides a natural way for semantic parsers to interact with the human experts. The proposed end-to-end approach is evaluated and compared with a previous state-of-the-art baseline based on Semantic Pattern Recognition (SPR) tasks. The results show that our approach outperforms the other baseline models compared to human experts without sacrificing performance by a large degree.


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