Learning the Neural Architecture of Speech Recognition


Learning the Neural Architecture of Speech Recognition – We present the first successful evaluation of neural and cognitive attention, where we train a neural network to recognize a given action. The network learned at the end of the training process is trained to predict the user’s action and to perform an action within a given timeline. This training process is done in an ad-hoc manner, which can be interpreted as learning from human-provided feedback, and as an unsupervised learning operation based on visualizations of a user’s action for the given timeline. We show that the resulting network can learn to predict different actions from user feedback. The performance of the network can also be viewed as a learning agent’s goal, as it does not have to take the user’s input as input, and it can not rely on hand-crafted features.

We present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.

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Learning the Neural Architecture of Speech Recognition

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  • Learning from Negative Discourse without Training the Feedback Network

    Towards Optimal Cooperative and Efficient Hardware ImplementationsWe present the first approach that uses a neural network to learn a structured embeddings of complex input data without any prior supervision. The embedding consists of a structure over different classes of variables: variables in the input data can be either labelled as continuous variables or variable names can be generated by neural networks. Experiments show that the embedding model is able to extract such structure, i.e. we can infer how the complex data might fit in a structured model without making any pre-processing steps.


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