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COMPUTATIONAL MODELS FOR LEARNING PITCH AND DURATION USING LST RECURRENT NEURAL NETWORKS Judy A. Franklin Computer Science Department Smith College Northampton, MA 01063 Franklin cs.smith.edu ABSTRACT

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Computational models for learning are mathematical or algorithmic representations of how learning takes place in a computational system. They are used to simulate and understand the processes of learning and to develop and optimize learning algorithms and methods.

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Filling out computational models for learning involves creating or implementing the mathematical or algorithmic representation of the learning process. This can include defining the learning objectives, designing the learning algorithms, and specifying the input data and output results for the computational model.

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The purpose of computational models for learning is to gain insights into how learning occurs in computational systems, to optimize learning algorithms and methods, and to develop and improve the performance of machine learning and artificial intelligence systems.

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The specific information that must be reported on computational models for learning can vary depending on the context and purpose of the model. However, typical information includes the mathematical or algorithmic representation of the learning process, the input data used for training and testing, the performance metrics used to evaluate the model, and any relevant parameters or assumptions made in the model.

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