Sparsity Regularized Generalized Recurrent Neural Networks – The paper proposes a method for training a Recurrent Neural Network (RNN) that is able to predict time dependencies between all the nodes and to predict the probability that a prediction may happen. This is a key step in the development of RNNs and is crucial to the state of the art research on RNNs, as well as many other models.
Deep learning has achieved massive success in many applications, such as computer vision. However, while the state of the art approaches on a range of such applications, none has benefited from the fact that such approaches are typically limited to single-object classification using a single model. This paper provides the first step towards this goal by proposing a hybrid architecture of two-manifold deep learning approaches which are specifically designed to perform object detection, which can be generalized to any other single-object classification task. We first describe a new approach that uses two-manifolds as the state space representation for object detection and then train our novel two-manifolds model to learn to classify multiple single objects. The second classifier is trained using a multi-stage LSTM, which is then used to obtain a robust prediction score for classifier selection.
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Sparsity Regularized Generalized Recurrent Neural Networks
Multi-Channel RGB-D – An Enhanced Deep Convolutional Network for Salient Object DetectionDeep learning has achieved massive success in many applications, such as computer vision. However, while the state of the art approaches on a range of such applications, none has benefited from the fact that such approaches are typically limited to single-object classification using a single model. This paper provides the first step towards this goal by proposing a hybrid architecture of two-manifold deep learning approaches which are specifically designed to perform object detection, which can be generalized to any other single-object classification task. We first describe a new approach that uses two-manifolds as the state space representation for object detection and then train our novel two-manifolds model to learn to classify multiple single objects. The second classifier is trained using a multi-stage LSTM, which is then used to obtain a robust prediction score for classifier selection.
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