Structured Prediction Theory Based on Factor Graph Complexity
Authors: Corinna Cortes, Vitaly Kuznetsov, Mehryar Mohri, Scott Yang
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also report the results of experiments with VCRF on several datasets to validate our theory. |
| Researcher Affiliation | Collaboration | Corinna Cortes Google Research New York, NY 10011 corinna@google.com Vitaly Kuznetsov Google Research New York, NY 10011 vitaly@cims.nyu.edu Mehryar Mohrii Courant Institute and Google New York, NY 10012 mohri@cims.nyu.edu Scott Yang Courant Institute New York, NY 10012 yangs@cims.nyu.edu |
| Pseudocode | No | The paper describes algorithms (VCRF, VStruct Boost) and refers to appendices for details, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We conducted experiments on several part-of-speech (POS) datasets: Penn Treebank (PTB), CONLL-2000, CONLL-2003, and Ontonotes. |
| Dataset Splits | Yes | We used the standard splits for PTB (sections 0-18 for training, 19-21 for validation, 22-24 for test), for CONLL-2000 (train and test), for CONLL-2003 (train, validation, and test), and for Ontonotes (train, validation, and test). |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU, GPU models, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of algorithms (e.g., VCRF, CRF) but does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For VCRF, we used a fixed learning rate of 0.001. |