Dr Robert Lieck's Outputs (31)

Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning (2025)
Presentation / Conference Contribution
Milton, T., & Lieck, R. (2024, December). Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning. Presented at 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Lisbon, Portugal

Type 1 diabetes is a chronic metabolic disease that requires regular insulin injections to regulate blood glucose levels. Recently, traditional manual approaches to diabetes management have been revolutionized by the use of continuous glucose monitor... Read More about Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning.

Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation (2024)
Journal Article
Zhang, F. X., Deng, J., Lieck, R., & Shum, H. P. (online). Adaptive Graph Learning from Spatial Information for Surgical Workflow Anticipation. IEEE Transactions on Medical Robotics and Bionics, https://doi.org/10.1109/TMRB.2024.3517137

Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history (2024)
Journal Article
Moss, F. C., Lieck, R., & Rohrmeier, M. (2024). Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history. Humanities and Social Sciences Communications, 11(1), Article 684. https://doi.org/10.1057/s41599-024-03168-1

Diachronic stylistic changes in music are to a large extent affected by composers’ different choices, for example regarding the usage of tones, intervals, and harmonies. Analyzing the tonal content of pieces of music and observing them over time is t... Read More about Computational modeling of interval distributions in tonal space reveals paradigmatic stylistic changes in Western music history.

Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks (2021)
Presentation / Conference Contribution
Lieck, R., & Rohrmeier, M. (2021, December). Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks. Presented at Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021

Discretisation and Continuity: Simulating the Emergence of Symbols in Communication Games (2021)
Presentation / Conference Contribution
Lieck, R., Wall, L., & Rohrmeier, M. A. (2021, December). Discretisation and Continuity: Simulating the Emergence of Symbols in Communication Games. Presented at Proceedings of the Annual Meeting of the Cognitive Science Society, Vienna, Austria

Discretisation and Continuity: The Emergence of Symbols in Communication (2021)
Journal Article
Lieck, R., & Rohrmeier, M. (2021). Discretisation and Continuity: The Emergence of Symbols in Communication. Cognition, 215, https://doi.org/10.1016/j.cognition.2021.104787

Modelling Hierarchical Key Structure With Pitch Scapes (2020)
Presentation / Conference Contribution
Lieck, R., & Rohrmeier, M. (2020, December). Modelling Hierarchical Key Structure With Pitch Scapes. Presented at Proceedings of the 21st International Society for Music Information Retrieval Conference, Montréal, Canada

AutoScale: Automatic and Dynamic Scale Selection for Live Jazz Improvisation (2020)
Presentation / Conference Contribution
Jaccard, T., Lieck, R., & Rohrmeier, M. (2020, December). AutoScale: Automatic and Dynamic Scale Selection for Live Jazz Improvisation. Presented at International Conference on New Interfaces for Musical Expression, Birmingham, United Kingdom

The Tonal Diffusion Model (2020)
Journal Article
Lieck, R., Moss, F. C., & Rohrmeier, M. (2020). The Tonal Diffusion Model. Transactions of the International Society for Music Information Retrieval, 3(1), 153-164. https://doi.org/10.5334/tismir.46

The Impact of Voice Leading and Harmony on Musical Expectancy (2020)
Journal Article
Wall, L., Lieck, R., Neuwirth, M., & Rohrmeier, M. (2020). The Impact of Voice Leading and Harmony on Musical Expectancy. Scientific Reports, 10(1), 1-8. https://doi.org/10.1038/s41598-020-61645-4

How Do We Build Practical Systems Involving Ethics and Trust? (2019)
Journal Article
Dennis, L. A., Tubella, A. A., Chatila, R., Duijf, H., Dyoub, A., Eder, K. I., Horty, J. F., Köhl, M., Lieck, R., & Singh, M. P. (2019). How Do We Build Practical Systems Involving Ethics and Trust?. Dagstuhl Reports, 9(4), 59-86. https://doi.org/10.4230/dagrep.9.4.59

A Model Comparison for Chord Prediction on the Annotated Beethoven Corpus (2019)
Presentation / Conference Contribution
Landsnes, K., Mehrabyan, L., Wiklund, V., Lieck, R., Moss, F. C., & Rohrmeier, M. (2019, May). A Model Comparison for Chord Prediction on the Annotated Beethoven Corpus. Presented at Proceedings of the 16th Sound \& Music Computing Conference, Málaga, Spain

Towards a Unified Model for Harmony and Voice-Leading (2018)
Other
Lieck, R., Harasim, D., & Rohrmeier, M. (2018). Towards a Unified Model for Harmony and Voice-Leading

Learning Structured Models of Musical Syntax (2018)
Other
Lieck, R., Harasim, D., & Rohrmeier, M. (2018). Learning Structured Models of Musical Syntax

Learning Structured Models for Active Planning: Beyond the Markov Paradigm Towards Adaptable Abstractions (2018)
Thesis
Lieck, R. (2018). Learning Structured Models for Active Planning: Beyond the Markov Paradigm Towards Adaptable Abstractions. (Thesis). Universität Stuttgart. Retrieved from https://durham-repository.worktribe.com/output/1641954

Feature Discovery for Sequential Prediction of Monophonic Music (2017)
Presentation / Conference Contribution
Langhabel, J., Lieck, R., Toussaint, M., & Rohrmeier, M. (2017, December). Feature Discovery for Sequential Prediction of Monophonic Music. Presented at Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR), Suzhou, China

Exploiting Variance Information in Monte-Carlo Tree Search (2017)
Presentation / Conference Contribution
Lieck, R., Ngo, V., & Toussaint, M. (2017, December). Exploiting Variance Information in Monte-Carlo Tree Search. Presented at ICAPS Workshop on Heuristics and Search for Domain-independent Planning

Active Tree Search (2017)
Presentation / Conference Contribution
Lieck, R., & Toussaint, M. (2017, December). Active Tree Search. Presented at ICAPS Workshop on Planning, Search, and Optimization

Towards Moral Autonomous Systems (2017)
Report
Charisi, V., Dennis, L., Fisher, M., Lieck, R., Matthias, A., Slavkovik, M., …Yampolskiy, R. (2017). Towards Moral Autonomous Systems. [No known commissioning body]

Cross-Entropy as a Criterion for Robust Interactive Learning of Latent Properties (2016)
Presentation / Conference Contribution
Kulick, J., Lieck, R., & Toussaint, M. (2016, December). Cross-Entropy as a Criterion for Robust Interactive Learning of Latent Properties. Presented at NIPS Workshop on the Future of Interactive Learning Machines