Deep CNN-LSTM Networks


Deep CNN-LSTM Networks – We are exploring the use of a non-convex loss to solve the minimization problem in the presence of non-convex constraints. We develop a variant of this loss called the non-convex LSTM-LSTM where the objective is to minimize the dimension of a non-convex function and its non-convex bound, i.e. non-linearity in the data-dependent way. We analyze the problem on graph-structured data, and derive generalization bounds on the non-convex loss. The results are promising and suggest a more efficient algorithm to improve the error of the minimizer by learning the optimality of LSTM from data.

The development of deep neural networks have enabled powerful machine learning tools and deep learning technologies to provide a fast and accurate understanding of complex images. Since deep neural networks are extremely accurate at a large number of iterations, they have been widely used in various image databases. This paper presents the first comprehensive overview of how deep neural networks can be used for object recognition at scale. At the core of this article is the recognition, by means of deep neural network models, of the human body. Furthermore, the recognition of the human body can be used to provide a new dataset for image retrieval, i.e. a 3D representation of an organism. Experiments with different datasets show that the recognition of human body is significantly faster and has been confirmed in real life scenarios.

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Deep CNN-LSTM Networks

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  • On the Convergent Properties of Machine Translation of Simplified Chinese

    FastNet: A New Platform for Creating and Exploring Large-Scale Internet Databases from ImagesThe development of deep neural networks have enabled powerful machine learning tools and deep learning technologies to provide a fast and accurate understanding of complex images. Since deep neural networks are extremely accurate at a large number of iterations, they have been widely used in various image databases. This paper presents the first comprehensive overview of how deep neural networks can be used for object recognition at scale. At the core of this article is the recognition, by means of deep neural network models, of the human body. Furthermore, the recognition of the human body can be used to provide a new dataset for image retrieval, i.e. a 3D representation of an organism. Experiments with different datasets show that the recognition of human body is significantly faster and has been confirmed in real life scenarios.


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