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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Discriminative Structure Learning of Arithmetic Circuits
Authors: Amirmohammad Rooshenas, Daniel Lowd
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on our experiments, DACLearn learns models that are more accurate and compact than other tractable generative and discriminative baselines. We run our experiments using 20 datasets with 16 to 1556 binary-valued variables |
| Researcher Affiliation | Academia | Amirmohammad Rooshenas and Daniel Lowd Department of Computer and Information Science University of Oregon Eugene, OR 97401, USA EMAIL |
| Pseudocode | Yes | Algorithm 1 shows the high-level pseudo-code of the DACLearn algorithm. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that the code for the methodology is available. |
| Open Datasets | Yes | We run our experiments using 20 datasets with 16 to 1556 binary-valued variables, which also used by Gens and Domingos (2013) and Rooshenas and Lowd (2014). |
| Dataset Splits | Yes | For all of the above methods, we learn the model using the training data and tune the hyper-parameters using the validation data, and we report the average CLL over the test data. |
| Hardware Specification | No | The paper mentions bounding learning time to 24 hours but does not provide specific details about the hardware used (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | To tune the hyper-parameters, we used a grid search over the hyper-parameter space. |