Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Probabilistic Rule Realization and Selection
Authors: Haizi Yu, Tianxi Li, Lav R. Varshney
NeurIPS 2017 | Venue PDF | 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 EMAIL Tianxi Li Department of Statistics University of Michigan Ann Arbor, MI 48109 EMAIL Lav R. Varshney Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana, IL 61801 EMAIL |
| 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. |