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glad
====== Differences ====== This shows you the differences between two versions of the page.
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glad [2013/06/30 22:45] dawenl |
glad [2013/07/01 18:31] (current) dawenl [3 Future work] |
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| - At the moment, only binary labels are supported. But in fact, the model is easily extended to handle multinomial labels. | - At the moment, only binary labels are supported. But in fact, the model is easily extended to handle multinomial labels. | ||
| - | - Now each individual label is treated completely independently. However, in the real world, it's easy to consider the correlation between different tags (e.g. "Rock" is definitely more positively-correlated to "Electric Guitar (Distortion)" than "Sampler"). This can be done by the similar idea from Correlated Topic Model ([[http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_774.pdf|CTM]]). | + | - Now each individual label is treated completely independent. However, in the real world, it's easy to consider the correlation between different tags (e.g. "Rock" is definitely more positively-correlated to "Electric Guitar (Distortion)" than "Sampler"). This can be done by the similar idea from Correlated Topic Model ([[http://machinelearning.wustl.edu/mlpapers/paper_files/NIPS2005_774.pdf|CTM]]). |
| - | - An interesting yet challenging problem would be to integrate the noisy beat annotation to create better ground truth data for beat tracking tasks. The main difference is that in beat annotation, the labels are no longer discretized category, instead they are temporally-dependent series, which will make the problem much more difficult. | + | - An interesting yet challenging problem would be to integrate the noisy beat annotations to create better ground truth data for beat tracking tasks. The main difference is that in beat annotation, the labels are no longer discretized categories, instead they are temporally-dependent series, which makes the problem much more difficult. |
glad.1372646746.txt.gz ยท Last modified: 2013/06/30 22:45 by dawenl
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