Multi-dimensional Recurrent Neural Networks for Music Genome Analysis


Multi-dimensional Recurrent Neural Networks for Music Genome Analysis – We present a deep attention-based framework for semantic image segmentation. Our approach is based on multi-class feature learning and learns the label pairs of the feature space given that each class is a vector of labels. We extend the supervised learning approach to perform segmentation by automatically learning the labels of labels, and then performing semantic segmentation as a step towards classification of labels in a shared feature space. Our approach improves both the classification and supervised learning performance of existing state-of-the-art semantic segmentation methods using only the label pairs. We demonstrate our approach for semantic segmentation and for image classification.

The goal of this paper is to propose a deep learning-based technique based on graph-structured graphical models for the optimization of optimal ranking. We propose to use Markov chain Monte Carlo (MCMC) to model the structure in graphs, and then a linear classifier based on the rank for each node, that achieves high ranking. We perform a regression analysis to evaluate the accuracy of our approach, which indicates that it converges significantly faster than the state-of-the-art method.

With increasing demands on neural network models, supervised object segmentation becomes increasingly important as well. In this work, we study the problem of automatically segmenting a dataset of object shapes in images and videos by means of supervised learning. We present a method that achieves very efficient segmentation of shapes, without sacrificing image quality or accuracy. For the sake of computational simplicity, we define a new set of supervised object segmentation criteria, and then develop an automatic segmentation algorithm for each shape.

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Multi-dimensional Recurrent Neural Networks for Music Genome Analysis

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  • Recurrent Topic Models for Sequential Segmentation

    A Convex Approach to Generalized Optimal RankingThe goal of this paper is to propose a deep learning-based technique based on graph-structured graphical models for the optimization of optimal ranking. We propose to use Markov chain Monte Carlo (MCMC) to model the structure in graphs, and then a linear classifier based on the rank for each node, that achieves high ranking. We perform a regression analysis to evaluate the accuracy of our approach, which indicates that it converges significantly faster than the state-of-the-art method.

    With increasing demands on neural network models, supervised object segmentation becomes increasingly important as well. In this work, we study the problem of automatically segmenting a dataset of object shapes in images and videos by means of supervised learning. We present a method that achieves very efficient segmentation of shapes, without sacrificing image quality or accuracy. For the sake of computational simplicity, we define a new set of supervised object segmentation criteria, and then develop an automatic segmentation algorithm for each shape.


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