Location-Based Activity Recognition with Hierarchical Dirichlet Process
Authors: Negar Ghourchian
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our unsupervised approach on two real-world datasets. |
| Researcher Affiliation | Academia | Negar Ghourchian Mc Gill University, Montreal, Canada negar@cs.mcgill.ca |
| Pseudocode | No | The paper describes the model and regularization steps using text and mathematical equations (e.g., equations 1 and 2), but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code for the described methodology, nor does it include links to any code repositories. |
| Open Datasets | Yes | Proposed approaches are evaluated on two datasets from MIT Reality Mining projects [Olgu ın et al., 2009; Eagle and Pentland, 2006]. |
| Dataset Splits | No | The paper mentions evaluating on the 'Badge Dataset' and 'Reality Mining Dataset', but it does not provide specific details on how these datasets were split into training, validation, or test sets for reproduction. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as GPU/CPU models, memory, or cloud instances. |
| Software Dependencies | No | The paper describes the proposed models and experiments but does not list any specific software dependencies or their version numbers. |
| Experiment Setup | No | The paper describes the model architecture and evaluation datasets but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size) or training configurations. |