Tractability in Structured Probability Spaces
Authors: Arthur Choi, Yujia Shen, Adnan Darwiche
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate the utility of our model empirically, in a route prediction task, showing how accuracy can be increased significantly compared to Markov models. and In our experiments, we considered a dataset consisting of GPS data collected from taxicab routes in San Francisco. |
| Researcher Affiliation | Academia | Arthur Choi University of California Los Angeles, CA 90095 aychoi@cs.ucla.edu Yujia Shen University of California Los Angeles, CA 90095 yujias@cs.ucla.edu Adnan Darwiche University of California Los Angeles, CA 90095 darwiche@cs.ucla.edu |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions several external tools and datasets, but it does not include an unambiguous statement that the authors are releasing the code for the work described in this paper, nor does it provide a direct link to such code. |
| Open Datasets | Yes | In our experiments, we considered a dataset consisting of GPS data collected from taxicab routes in San Francisco.8 ... 8Available at http://crawdad.org/epfl/mobility/20090224/. |
| Dataset Splits | No | We used half for training, and the other half for testing. The paper specifies a train/test split but does not mention a validation set. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions software like the 'graphhopper package' and 'GRAPHILLION library' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper mentions 'assuming Laplace smoothing' but does not provide specific details on hyperparameters, training configurations, or system-level settings for the experimental setup. |