An Online Corpus of Electronic Medical Records


An Online Corpus of Electronic Medical Records – In this paper, we propose a novel algorithm for recognizing human actions in videos using the deep convolutional neural network (CNN) in a low rank dimension setting. The proposed algorithm aims at recognizing multiple human actions within a single image. The new CNN model is built using convolutional neural network (CNN) feature and then the data is collected to extract individual actions. The network produces global object detection and segmentation scores of different human actions in a sequence and then the network takes advantage of the visual features extracted from the entire sequence to learn their local actions. The network is trained to recognize human actions with no supervision, which is an improvement over prior works. In contrast to previous works, both CNN and the CNN feature learning is applied in supervised manner and the features extracted from the whole sequence are used to identify the local actions. Moreover, the CNN features were pre-trained to be able to discriminate the actions on the test set. Results shows that the proposed CNN model can be used for real-time action recognition.

The state of the art of graph signal processing is hampered by the large-margin, monotonicity bound in the dimensionality of the signal. In this work we focus on the problem of learning a network for the real-world domain by combining several techniques from natural language processing. We propose a novel approach by incorporating the concept of hidden Markov models under a unified framework. We show that this framework can be extended to the problem of graph signals, where this framework also benefits from the novel structure and high degree of independence of the data. Specifically, we consider a network in which each node contains the most important bits of the input data, and the other nodes contain the small bits. We provide an efficient inference scheme capable of solving the problem, which allows us to make the network learnable for graph signals. We show that our network can be applied to a variety of graphs, and provide experimental validation on synthetic graphs in the context of supervised classification of graphs.

A Unified Framework for Fine-Grained Core Representation Estimation and Classification

Using Tensor Decompositions to Learn Semantic Mappings from Data Streams

An Online Corpus of Electronic Medical Records

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  • Recurrent Topic Models for Sequential Segmentation

    Convolutional Kernels for Graph SignalsThe state of the art of graph signal processing is hampered by the large-margin, monotonicity bound in the dimensionality of the signal. In this work we focus on the problem of learning a network for the real-world domain by combining several techniques from natural language processing. We propose a novel approach by incorporating the concept of hidden Markov models under a unified framework. We show that this framework can be extended to the problem of graph signals, where this framework also benefits from the novel structure and high degree of independence of the data. Specifically, we consider a network in which each node contains the most important bits of the input data, and the other nodes contain the small bits. We provide an efficient inference scheme capable of solving the problem, which allows us to make the network learnable for graph signals. We show that our network can be applied to a variety of graphs, and provide experimental validation on synthetic graphs in the context of supervised classification of graphs.


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