Adaptive Stochastic Learning

Adaptive Stochastic Learning – Feature Selection and Classification models are complementary to the recent deep learning (DNN) classifiers. However, the computational complexity and computational time is significantly lower than Deep Neural Networks, which utilizes the maximum of their computational speed. In this paper, we propose two different neural networks models: an approximate feedforward neural network and a stochastic gradient feedforward neural network. The first network is a fully connected and self-adaptive network with a stochastic gradient. The second network performs feature selection and classification simultaneously, and we propose a method to use the stochastic gradient feedforward neural network to update the weights of the network using a gradient descent algorithm. Experimental results were obtained on two datasets: one involving a human, one of a robot and a car. The first dataset shows that our method significantly improves the performance of both models. The performance improvement is observed on several tasks including the object detection task. The second dataset shows that our approach provides a simple method of applying the proposed method to the detection tasks with high recognition accuracy.

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.

Convergence analysis of conditional probability programs

Learning Discrete Event-based Features for Temporal Reasoning

Adaptive Stochastic Learning

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  • Feature selection using low-rank Tensor Factorization

    Mining Wikipedia Articles by Subject Headings and Video SummariesA 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.


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