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His particular interest in the field of computational creativity addresses the question of whether or not computers, beyond possessing artificial intelligence, can exhibit autonomous creativity. In particular, he has developed expertise in the domain of lyrical music composition and the challenge of invoking long-term structure in sequence generation. His approach incorporates a modular machine learning framework called hierarchical Bayesian program learning, which facilitates breaking the problem of music composition into smaller pieces, and focuses primarily on developing machine learning models that solve the problems related to structure. He has developed an adaptation of non-homogenous Markov models that enables long-range constraints and a structural learning model adapted from the Smith-Waterman alignment method, which extends sequence alignment techniques from bioinformatics. He has incorporated these advances into a full-fledged computational creative system called Pop* (pronounced popstar) and has shown through various evaluative methods that the system can be argued to possess, to varying degrees, the characteristics of creativity. Professor Bodily's prior research also includes several bioinformatics publications on heterozygous genome assembly algorithms.
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论文共 56 篇作者统计合作学者相似作者
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AAAI 2024no. 1 (2024): 447-455
2023 IEEE International Conference on Big Data (BigData)pp.5799-5808, (2023)
2022 Intermountain Engineering, Technology and Computing (IETC)pp.1-6, (2022)
2022 Intermountain Engineering, Technology and Computing (IETC)pp.1-4, (2022)
2022 Intermountain Engineering, Technology and Computing (IETC)pp.1-5, (2022)
2022 Intermountain Engineering, Technology and Computing (IETC)pp.1-5, (2022)
Transactions of the International Society for Music Information Retrievalno. 1 (2022): 71-86
2022 Intermountain Engineering, Technology and Computing (IETC)pp.1-6, (2022)
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