Name
Abstract | ||
|---|---|---|
Bipartite matching problems characterize many situations, ranging from ranking in information retrieval to correspondence in vision. Exact inference in real-world applications of these problems is intractable, making efficient approximation methods essential for learning and inference. In this paper we propose a novel {\it sequential matching} sampler based on the generalization of the Plackett-Luce model, which can effectively make large moves in the space of matchings. This allows the sampler to match the difficult target distributions common in these problems: highly multimodal distributions with well separated modes. We present experimental results with bipartite matching problems - ranking and image correspondence - which show that the sequential matching sampler efficiently approximates the target distribution, significantly outperforming other sampling approaches. |
Year | Venue | DocType |
|---|---|---|
2012 | NIPS | Conference |
Citations | PageRank | References |
2 | 0.38 | 14 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maksims Volkovs | 1 | 216 | 14.48 |
| Richard S. Zemel | 2 | 4958 | 425.68 |