A Framework for Automated Knowledge Representation and Construction in Machine Learning: Project Description and Dataset


A Framework for Automated Knowledge Representation and Construction in Machine Learning: Project Description and Dataset – In this paper, we present a new framework for learning neural architectures from data, called Deep Recurrent Neural Networks (DRNNs). Instead of simply learning one state-of-the-art deep architectures, we describe a new approach which simultaneously learns to learn the state-of-the-art models, and to make the models adapt to the environment. This approach has been demonstrated against a variety of state-of-the-art architectures, and has been extensively compared against other models. In this work, we are able to show that DNNs can be trained to learn the state-of-the-art deep architectures in an efficient manner that can be applied to a variety of tasks.

Recently, deep learning based computer vision and object tracking systems have attracted the attention of researchers and practitioners. In this work, we propose a novel deep learning methodology, that takes the latent structure of the world (images) and produces an action-like representation for the visual representation by using multiple deep networks. We first propose an approximate and approximate representation of the world by constructing a network that learns to interpret a set of images as their target state in a single actionable graph. The network adaptively combines the state of these images with the action-like representation of a target world to form an actionable representation. We then use the action-like representation to learn the action recognition model via a visualization process for each object in the image, in order to further improve the recognition performance. Experimental results in two datasets show that our proposal is able to outperform state-of-the-art recognition methods even on the very challenging case of large datasets.

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A Framework for Automated Knowledge Representation and Construction in Machine Learning: Project Description and Dataset

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  • Fast Recurrent Neural Networks for Video Generation

    A Unified Approach to Visual Problem of Data Sharing and Sharing-of-information for Personalized RecommendationsRecently, deep learning based computer vision and object tracking systems have attracted the attention of researchers and practitioners. In this work, we propose a novel deep learning methodology, that takes the latent structure of the world (images) and produces an action-like representation for the visual representation by using multiple deep networks. We first propose an approximate and approximate representation of the world by constructing a network that learns to interpret a set of images as their target state in a single actionable graph. The network adaptively combines the state of these images with the action-like representation of a target world to form an actionable representation. We then use the action-like representation to learn the action recognition model via a visualization process for each object in the image, in order to further improve the recognition performance. Experimental results in two datasets show that our proposal is able to outperform state-of-the-art recognition methods even on the very challenging case of large datasets.


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