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Network Simplification Through Oracle Learning, Tony R. Martinez, Joshua Menke, Adam Peterson, Michael E. Rimer
Network Simplification Through Oracle Learning, Tony R. Martinez, Joshua Menke, Adam Peterson, Michael E. Rimer
Faculty Publications
Often the best artificial neural network to solve a real world problem is relatively complex. However, with the growing popularity of smaller computing devices (handheld computers, cellular telephones, automobile interfaces, etc.), there is a need for simpler models with comparable accuracy. The following research presents evidence that using a larger model as an oracle to train a smaller model on unlabeled data results in 1) a simpler acceptable model and 2) improved results over standard training methods on a similarly sized smaller model. On automated spoken digit recognition, oracle learning resulted in an artificial neural network of half the size …
Complexity And Heuristics In Ruled-Based Algorithmic Music Composition, Nigel Gwee
Complexity And Heuristics In Ruled-Based Algorithmic Music Composition, Nigel Gwee
LSU Doctoral Dissertations
Successful algorithmic music composition requires the efficient creation of works that reflect human preferences. In examining this key issue, we make two main contributions in this dissertation: analysis of the computational complexity of algorithmic music composition, and methods to produce music that approximates a commendable human effort. We use species counterpoint as our compositional model, wherein a set of stylistic and grammatical rules governs the search for suitable countermelodies to match a given melody. Our analysis of the complexity of rule-based music composition considers four different types of computational problems: decision, enumeration, number, and optimization. For restricted versions of the …