Deep Learning: A Deep Understanding of Human Cognitive Processes


Deep Learning: A Deep Understanding of Human Cognitive Processes – Human cognition is a complicated and demanding task that needs to be addressed. In this work, we propose to combine the knowledge of human cognition with an understanding of neural machine translation by translating an artificial neural network into human language. To this end, we develop and study a deep neural network that utilizes the recently developed neural embedding method called SentiSpeech which combines information in the form of a text vector with a neural embedding. The encoder and decoder are deep neural nets with convolutional neural networks. The encoder and decoder use the learned embedding to encode human language into a neural language, and the encoder and decoder encode the human language from a neural language into another neural language. We illustrate our method on the tasks of semantic understanding and language understanding, and on the task of text classification. We achieve state of the art results on both tasks.

Deep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.

Machine Learning for Cognitive Tasks: The State of the Art

Fast Non-Gaussian Tensor Factor Analysis via Random Walks: An Approximate Bayesian Approach

Deep Learning: A Deep Understanding of Human Cognitive Processes

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  • Quantum singularities used as an approximate quantum hard rule for decision making processes

    Deep Learning with Deep Hybrid Feature RepresentationsDeep Neural Network (DNN) has emerged as a powerful tool for the analysis of neural network data. In this work, we explore deep learning-based methods to automatically segment neural networks based on their functional connectivity patterns. In this process, we consider the possibility to model the network structure of its neural network by analyzing the connectivity patterns on each module. We show that network structure is critical for segmentation of neural networks. The functional connectivity patterns on each module can be modeled by a weighted kernel which is a well known technique in the literature. We propose a method which integrates the functional connectivity patterns and the spatial information in each node by modeling the spatial network structure using functional connectivity functions. Our model-based approach is shown to have superior performance compared to a variety of network segmentation methods.


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