Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
Authors: Anton Osokin, Jean-Baptiste Alayrac, Isabella Lukasewitz, Puneet Dokania, Simon Lacoste-Julien
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an exhaustive empirical evaluation of all our methods on four structured prediction datasets. |
| Researcher Affiliation | Academia | 1INRIA Ecole Normale Sup erieure, Paris, France 2INRIA Centrale Sup elec, Chˆatenay-Malabry, France |
| Pseudocode | Yes | Algorithm 1 Block-Coordinate Frank-Wolfe (BCFW) algorithm for structured SVM |
| Open Source Code | Yes | The code and datasets are available at our project webpage.2 2 http://www.di.ens.fr/sierra/research/gap BCFW/ |
| Open Datasets | Yes | We evaluate our methods on four datasets for different structured prediction tasks: OCR (Taskar et al., 2003) for handwritten character recognition, Co NLL (Tjong Kim Sang & Buchholz, 2000) for text chunking, Horse Seg (Kolesnikov et al., 2014) for binary image segmentation and LSP (Johnson & Everingham, 2010) for pose estimation. |
| Dataset Splits | No | The paper mentions using a 'test performance' value for λ, but does not provide specific train/validation/test split percentages or sample counts for any of the datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., CPU/GPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | We report the results of each method on 6 datasets (including 3 sizes of Horse Seg) for three values of the regularization parameter λ: the value leading to the best test performance, a smaller and a larger value. For each setup, we report the duality gap against both number of oracle calls and elapsed time. We run each method 5 times with different random seeds influencing the order of sampled objects and report the median (bold line), minimum and maximum values (shaded region). |