Embedding Inference for Structured Multilabel Prediction
Authors: Farzaneh Mirzazadeh, Siamak Ravanbakhsh, Nan Ding, Dale Schuurmans
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate that the benefits of structured output training can still be realized even after inference has been eliminated. |
| Researcher Affiliation | Collaboration | Farzaneh Mirzazadeh Siamak Ravanbakhsh University of Alberta {mirzazad,mravanba}@ualberta.ca Nan Ding Google dingnan@google.com Dale Schuurmans University of Alberta daes@ualberta.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | In particular, we investigated three multilabel text classification data sets, Enron, WIPO and Reuters, obtained from https://sites. google.com/site/hrsvmproject/datasets-hier (see Table 1 for details). |
| Dataset Splits | No | The paper does not provide specific details on training, validation, or test dataset splits, beyond referring to a 'test set'. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions LBFGS and a bundle method but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In each case, the regularization parameter was simply set to 1. |