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Shared Features in Log-Linear Models

Representation. Probabilistic Graphical Models. Template Models. Shared Features in Log-Linear Models. Ising Models. In most MRFs, same feature and weight are used over many scopes. Ising Model. same weight for every adjacent pair. Natural Language Processing.

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Shared Features in Log-Linear Models

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  1. Representation Probabilistic Graphical Models Template Models Shared Features in Log-Linear Models

  2. Ising Models • In most MRFs, same feature and weight are used over many scopes Ising Model same weight for every adjacent pair

  3. Natural Language Processing • In most MRFs, same feature and weight are used over many scopes Yi Xi Same energy terms wkfk(Xi,Yi) repeat for all positions i in the sequence Same energy terms wmfm(Yi,Yi+1) a;sp repeat for all positions i

  4. Image Segmentation • In most MRFs, same feature and weight are used over many scopes Same features and weights for all superpixels in the image

  5. Repeated Features • Need to specify for each feature fk a set of scopes Scopes[fk] • For each DkScopes[fk] we have a term wkfk(Dk) in the energy function

  6. Summary • Same feature & weight can be used for multiple subsets of variables • Pairs of adjacent pixels/atoms/words • Occurrences of same word in document • Can provide a single template for multiple MNs • Different images • Different sentences • Parameters and structure are reused within an MN and across different MNs • Need to specify set of scopes for each feature

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