Probabilistic Rule Realization and Selection
Authors: Haizi Yu, Tianxi Li, Lav R. Varshney
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run experiments on artificial rule sets to illustrate the operational characteristics of our model, and further test it on a real rule set that is exported from an automatic music theorist [11], demonstrating the model s efficiency in not only music realization (composition) but also music interpretation and understanding (analysis). |
| Researcher Affiliation | Academia | Haizi Yu Department of Computer Science University of Illinois at Urbana-Champaign Urbana, IL 61801 haiziyu7@illinois.edu Tianxi Li Department of Statistics University of Michigan Ann Arbor, MI 48109 tianxili@umich.edu Lav R. Varshney Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 varshney@illinois.edu |
| Pseudocode | No | The paper describes algorithms and formulations using mathematical equations and text, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions using 'artificial rule sets' and 'a real compositional rule set exported from an automatic music theorist [11]' but does not provide concrete access information (e.g., a link, DOI, or explicit statement of public availability) for these datasets. |
| Dataset Splits | No | The paper discusses tuning hyperparameters and evaluating model behavior, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., programming languages, libraries, or solvers with their versions) that would be needed to replicate the experiments. |
| Experiment Setup | No | The paper discusses tuning hyperparameters λw and α, stating 'for all experiments herein, we fix α = 0.8' and how λw is varied. However, it does not provide a comprehensive experimental setup including details like optimizer, learning rate, batch size, number of epochs, or other system-level training configurations. |