Learning from OWL annotations: using deep convolutional neural network techniques to predict the behavior of non-native learners


Learning from OWL annotations: using deep convolutional neural network techniques to predict the behavior of non-native learners – Recently, we have discovered the existence of a system that enables users to automatically learn an object’s meaning. This is a challenging problem which requires a rich set of visual features which have to be learned from user data, such as scales, curves, and shape. In this paper, we propose a novel neural network-based approach that utilizes the visual information to achieve better learning. The approach has been evaluated in the context of learning by hand and as a first step towards learning a specific visual object. We demonstrate that the learning model is capable of learning from user data. We further validate the learning with the supervised learning task by experimentally observing the performance of the existing Novello-Roo and ROUGE-U-101 visual object recognition systems, and the performance of the first stage of the system.

In this paper, we propose an efficient multi-layer deep neural network (MLNN) that can effectively predict the object appearance in images from both spatial-temporal (textured) and object-level (non-textured) gradients. Unlike prior works that assume the object-image information to be sparse, we use the same generalization error to learn and train the network. We show that this model can be used for many other tasks, such as image classification, object detection, human shape recognition, and object manipulation.

Machine learning algorithms and RNNs with spatiotemporal consistency

Fast, Accurate and High Quality Sparse Regression by Compressed Sensing with Online Random Walks in the Sparse Setting

Learning from OWL annotations: using deep convolutional neural network techniques to predict the behavior of non-native learners

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    Learning a Multilayer Neural Network with a Spiking Context-Behavior Algorithm for Image RecognitionIn this paper, we propose an efficient multi-layer deep neural network (MLNN) that can effectively predict the object appearance in images from both spatial-temporal (textured) and object-level (non-textured) gradients. Unlike prior works that assume the object-image information to be sparse, we use the same generalization error to learn and train the network. We show that this model can be used for many other tasks, such as image classification, object detection, human shape recognition, and object manipulation.


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