File:2013 LearningDeepStructuredSemanticM Fig1.png
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Summary
Figure 1: Illustration of the DSSM. It uses a DNN to map high-dimensional sparse text features into low-dimensional dense features in a semantic space. The first hidden layer, with 30k units, accomplishes word hashing. The word-hashed features are then projected through multiple layers of non-linear projections. The final layer’s neural activities in this DNN form the feature in the semantic space. In: Huang et al. (2013)
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| Date/Time | Thumbnail | Dimensions | User | Comment | |
|---|---|---|---|---|---|
| current | 23:47, 2 June 2019 | | 1,027 × 424 (98 KB) | Omoreira (talk | contribs) | '''Figure 1:''' Illustration of the DSSM. It uses a DNN to map high-dimensional sparse text features into low-dimensional dense features in a semantic space. The first hidden layer, with 30k units, accomplishes [[word ha... |
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