Mining Wikipedia Articles by Subject Headings and Video Summaries

Mining Wikipedia Articles by Subject Headings and Video Summaries – A new approach to automatically predicting the topics of articles on Wikipedia has been proposed by our co-investigators. We show that the prediction of the articles by topic alone produces promising results for a variety of applications beyond English Wikipedia. The goal is to predict the topic of articles on Wikipedia in a manner comparable to the ones produced by a prior knowledge base or the work of specialists in the field, including a large collection of existing research papers that cover a large range of topics. We propose here a new knowledge base that consists of two parts, a Knowledge Base Graph (KB) and a Learning Model. The KB helps to determine the topic of articles at a high level by predicting the number of citations to each article in the paper, which is then inferred to be an article’s topic. The KB predicts the topic of individual articles by identifying topic keywords for each article as it has been identified by a previous article. Experiments performed on the MNIST, AIM-SARIA and OMBR datasets demonstrate that the proposed method provides a promising performance.

In this paper we tackle the problem of learning a stochastic gradient descent algorithm for the same problem as learning a linear gradient. We apply this problem to neural networks, and show that our gradient descent algorithm has the best learning ability when the network is composed of different features. We further show that this algorithm performs better when the network is composed of multiple features, and that this is the case when the feature spaces are sampled from the data. To the best of our knowledge this is the first attempt to study stochastic gradient descent in a neural network context.

Deep Learning for Classification

Image denoising by additive fog light using a deep dictionary

Mining Wikipedia Articles by Subject Headings and Video Summaries

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  • Optimal Regret Bounds for Gaussian Processical Least Squares

    On the Complexity of Stochastic Gradient DescentIn this paper we tackle the problem of learning a stochastic gradient descent algorithm for the same problem as learning a linear gradient. We apply this problem to neural networks, and show that our gradient descent algorithm has the best learning ability when the network is composed of different features. We further show that this algorithm performs better when the network is composed of multiple features, and that this is the case when the feature spaces are sampled from the data. To the best of our knowledge this is the first attempt to study stochastic gradient descent in a neural network context.


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