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Speaker: [http://www.cs.umd.edu/~dchen/ Daozheng Chen] -- Date: March 29, 2012

Speaker: [http://www.cs.umd.edu/~dchen/ Daozheng Chen] -- Date: March 29, 2012

Latent variables models have been widely applied in many problems in machine learning and related fields such as computer vision and information retrieval.However, the complexity of the latent space in such models is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to overfitting and are slower to perform inference with. The goal of this work is to regularize the complexity of the latent space and \emph{learn} which hidden states are really relevant for the prediction problem.To this end, we propose regularization with a group norm such as $\ell_{1}$-$\ell_{2}$ to estimate parameters of a Latent Structural SVM. Our experiments on digit recognition show that our approach is indeed able to control the complexity of latent space, resulting in significantly faster inference at test-time without any loss in accuracy of the learnt model.

Latent variables models have been widely applied in many problems in machine learning and related fields such as computer vision and information retrieval.However, the complexity of the latent space in such models is typically left as a free design choice. A larger latent space results in a more expressive model, but such models are prone to overfitting and are slower to perform inference with. The goal of this work is to regularize the complexity of the latent space and learn which hidden states are really relevant for the prediction problem.To this end, we propose regularization with a group norm such as L1-L2 to estimate parameters of a Latent Structural SVM. Our experiments on digit recognition show that our approach is indeed able to control the complexity of latent space, resulting in significantly faster inference at test-time without any loss in accuracy of the learnt model.