NeurIPS 2019 Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center "6594" "A Primal Dual Formulation For Deep Learning With Constraints" All reviewers were positive about the contributions in the paper so I recommend acceptance. Please take into account all the reviewers' comments when preparing the final version of the paper.

Lee at al. 2017 (and the AAAI-19 version) deal only with the constrained inference, not learning. Yes, all our constraints are linguistically31 important. The goal of our expts was different from Mehta's. We obtained 69.11 using CL39 (supervised), which is 1.09 pt higher than their reported 68.02 for CL.

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By predicting the user's intended

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Facial action units (AUs) recognition is essential for emotion analysis and has been widely applied in mental state analysis. Existing work on AU recognition usually requires big face dataset with accurate AU labels. However, manual AU annotation requires expertise and can be time-consuming. In this work, we propose asemi-supervised approach forAUrecognition utilizing alargenumber of web face images without AU labels and a small face dataset with AU labels inspired by the co-training methods.

However, SBC has drawbacks: The test statistic is somewhat ad-hoc, interactions are difficult to examine, multiple testing is a challenge, and the resulting p-value is not a divergence metric.

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