Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Co-Regularized PLSA for Multi-Modal Learning
Authors: Xin Wang, MingChing Chang, Yiming Ying, Siwei Lyu
AAAI 2016 | Venue PDF | LLM Run Details | Input Tokens: 15,591 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,068 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the co PLSA algorithms on text/image cross-modal retrieval tasks, on which they show competitive performance with state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Department of Computer Science, 2Department of Mathematics and Statistics University at Albany, State University of New York Albany, NY 12222 |
| Pseudocode | Yes | The right panel of Fig.1 provides the pseudo-code of the overall algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use two benchmark text/image datasets in our experiments: TVGraz (Khan, Saffari, and Bischof 2009) and Wikipedia (Rasiwasia et al. 2010) |
| Dataset Splits | No | The paper mentions 'choose the balance parameter λ in co PLSA algorithms with cross-validation on a subset of the training data,' but it does not specify a distinct validation split (e.g., 80/10/10) for evaluating the model during training. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper mentions 'Lambert W function... is provided in popular numerical tools such as MATLAB (function lambertw) or Sci Py (function scipy.special.lambertw),' but it does not provide specific version numbers for MATLAB or SciPy. |
| Experiment Setup | Yes | We extract 50 topics from the TVGraz dataset and 100 topics from the Wikipedia dataset, and choose the balance parameter λ in co PLSA algorithms with cross-validation on a subset of the training data. |