Including a Belief Function in a Deep Generative Feature Learning Network


Including a Belief Function in a Deep Generative Feature Learning Network – Deep learning has recently shown considerable success in various fields of human-computer interaction. However, the most important problem that we face in deep learning is learning a human brain. To solve this problem, we propose using Convolutional Neural Networks (CNNs) for feature extraction and learning of multiple entities. In addition, we propose an end-to-end learning method for training CNNs, which can be seen as a method of self-organising the output. In this paper, we propose a different learning method combining a CNN for feature extraction and CNN for learning to model multiple entities, together with a Deep Learning Network for the output.

We present an end-to-end learning framework for learning to correctly predict the performance of human action recognition. We use an existing classifier, that is a hand-crafted object recognition approach. A simple, yet powerful algorithm based on a large dictionary of labeled objects is used for this task, and we apply this learning framework to improve our decision-making in the task of hand-crafted object recognition. Our experiments demonstrate that our proposed technique significantly improves the performance of the hand recognition task. Further, it can be applied to any hand-crafted object recognition task.

Learning and Querying Large Graphs via Active Hierarchical Reinforcement Learning

Unsupervised Domain Adaptation with Graph Convolutional Networks

Including a Belief Function in a Deep Generative Feature Learning Network

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  • Bias-Aware Recommender System using Topic Modeling

    Improving the performance of batch selection algorithms trained to recognize handwritten digitsWe present an end-to-end learning framework for learning to correctly predict the performance of human action recognition. We use an existing classifier, that is a hand-crafted object recognition approach. A simple, yet powerful algorithm based on a large dictionary of labeled objects is used for this task, and we apply this learning framework to improve our decision-making in the task of hand-crafted object recognition. Our experiments demonstrate that our proposed technique significantly improves the performance of the hand recognition task. Further, it can be applied to any hand-crafted object recognition task.


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