A Generative framework for Neural Networks in Informational and Personal Exploration


A Generative framework for Neural Networks in Informational and Personal Exploration – Robots have become a major part of the contemporary global economy, and their capability to carry out tasks for people and services will be critical to their survival. One of the most important challenges for robot technology is to adapt to the demands of the environment, in particular in the digital age. This requires the application of intelligent robotics to the task of environmental management based upon the spatial and temporal information of human spatial awareness. In this paper, we focus on the problem of sensing spatial awareness at the spatial level by integrating an encoder on the spatiotemporal side called the spatiotemporal data stream. In this work, we propose the first method to model spatial awareness at the spatial layer, in which the data stream is represented as a continuous space with multiple spatial layers. In this way, we model spatial awareness at different spatiotemporal levels using spatial cues from a spatiotemporal information stream from a video stream. The results of experiments show that the proposed method can capture spatial awareness at the spatial layer by using spatial cues from a video stream.

In this paper, we propose an end-to-end, fully convolution network which allows for efficient extraction of the low-level information in speech and visual data. The proposed model is a multi-stage, fully convolutional network and utilizes the convolutional layers together to learn a hierarchical representation. After learning, the extracted high-level information is used as a discriminator for inferring the audio patterns to be extracted, and then a sequence of the high-level information is then extracted from the discriminator. Based on the proposed model, the neural network is trained without any additional preprocessing step. To the best of our knowledge, this is the first fully-convolutional neural network that can be used for speech retrieval tasks.

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A Generative framework for Neural Networks in Informational and Personal Exploration

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  • Graphical learning via convex optimization: Two-layer random compositionality

    Adaptive Sparse Convolutional Features For Deep Neural Network-based Audio ClassificationIn this paper, we propose an end-to-end, fully convolution network which allows for efficient extraction of the low-level information in speech and visual data. The proposed model is a multi-stage, fully convolutional network and utilizes the convolutional layers together to learn a hierarchical representation. After learning, the extracted high-level information is used as a discriminator for inferring the audio patterns to be extracted, and then a sequence of the high-level information is then extracted from the discriminator. Based on the proposed model, the neural network is trained without any additional preprocessing step. To the best of our knowledge, this is the first fully-convolutional neural network that can be used for speech retrieval tasks.


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