Convex Co-embedding
Authors: Farzaneh Mirzazadeh, Yuhong Guo, Dale Schuurmans
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | An experimental evaluation reveals the advantages of global training in different case studies. |
| Researcher Affiliation | Academia | Farzaneh Mirzazadeh Department of Computing Science University of Alberta Edmonton, AB T6G 2E8, Canada mirzazad@cs.ualberta.ca; Yuhong Guo Department of Computer and Information Sciences, Temple University Philadelphia, PA 19122, USA yuhong@temple.edu; Dale Schuurmans Department of Computing Science University of Alberta Edmonton, AB T6G 2E8, Canada dale@cs.ualberta.ca |
| Pseudocode | No | The paper describes algorithms and methods but does not include a clearly labeled pseudocode block or algorithm figure. |
| 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 | The paper uses the multi-label data sets Corel5K, Emotion, Mediamill, Scene, and Yeast (Table 1), which are well-known benchmark datasets. For the tag recommendation, it uses 'Bib Sonomy' data, following '(J aschke et al. 2008) we exploit the core at level 10 subsample'. |
| Dataset Splits | No | The paper states: 'we used 1000 examples for training and the rest for testing (except Emotion where we used a 2/3 train-test split), repeating 10 times for different random splits.' It does not explicitly mention a separate validation split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific details about ancillary software, such as library names with version numbers. |
| Experiment Setup | Yes | The paper specifies hyperparameters such as regularization parameters λ (e.g., 'a common regularization parameter λ = λ1 = λ2 to the trace and squared Frobenius norm regularizers'), mentions specific loss functions ('smoothed version (28) of the large margin multi-label loss (27)', 'ranking logistic loss function'), and discusses initialization strategies ('random initialization', 'initializing with all 0s', 'initializing from all 1s'). Specific λ values are provided in Table 2 and Table 3. |