Learning Spatial and Sparse Generative Models with an Application to Machine Reading Comprehension


Learning Spatial and Sparse Generative Models with an Application to Machine Reading Comprehension – Deep learning is rapidly approaching the state-of-the-art in many computer vision tasks. It has been an open problem for many years and deep learning technology is not yet able to solve many applications. In this paper, we investigate two important questions: (1) Can deep learning and other architectures solve the problem of knowledge discovery in image segmentation? (2) What type of architecture can be used to tackle these two questions? Our objective is to design a new deep learning architecture that solves the two questions. We propose a simple framework which is capable to solve the two questions, and we propose a deep learning architecture that improves the performance of image segmentation problems by exploiting the learned priors. We test our framework on a set of image segmentation tasks. The proposed architecture achieves a significant improvement in efficiency over existing deep learning architectures in the segmentation task.

One of major difficulties for learning language from textual data is the fact that it is the learner who is motivated to learn the most relevant features in the data as they are typically most studied in a machine-learned language. In this paper we investigate two approaches for this research. First, by constructing a model from textual features of the data it helps guide the learner in learning features from a representation which can be a neural network model and a machine learning framework. We evaluate our methods in a variety of situations including the task of learning a system of English-to-German and English-to French-to Spanish sentences. In experiments on benchmark datasets, we show that the learned features are capable of representing the language as well as the human brain.

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Learning Spatial and Sparse Generative Models with an Application to Machine Reading Comprehension

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  • Deep Multi-Scale Multi-Task Learning via Low-rank Representation of 3D Part Frames

    Learning Lévy Grammars on GPUOne of major difficulties for learning language from textual data is the fact that it is the learner who is motivated to learn the most relevant features in the data as they are typically most studied in a machine-learned language. In this paper we investigate two approaches for this research. First, by constructing a model from textual features of the data it helps guide the learner in learning features from a representation which can be a neural network model and a machine learning framework. We evaluate our methods in a variety of situations including the task of learning a system of English-to-German and English-to French-to Spanish sentences. In experiments on benchmark datasets, we show that the learned features are capable of representing the language as well as the human brain.


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