Conditional PSDDs: Modeling and Learning With Modular Knowledge
Authors: Yujia Shen, Arthur Choi, Adnan Darwiche
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now empirically evaluate our proposed approach using conditional PSDDs by contrasting it to the classical way in which PSDDs are used. ... Figure 9 highlights the results. On the x-axis, we increase the size of the training set used. On the y-axis, we evaluate the test-set log likelihood (larger is better). |
| Researcher Affiliation | Academia | Yujia Shen, Arthur Choi, Adnan Darwiche Computer Science Department University of California, Los Angeles {yujias,aychoi,darwiche}@cs.ucla.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper mentions generating random datasets and using GPS data, but does not provide concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset. |
| Dataset Splits | No | The paper specifies training and test set sizes ('increased the size of training sets from 2^6 to 2^14, and used an independent test set of size 2^12') but does not mention a separate validation set or split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes how the synthetic data and network structures were generated for experiments, but it does not provide specific experimental setup details such as hyperparameters or training configurations for the learning algorithm itself. |