From #routing to #hierarchical multi-label classification and user #preference learning, SPLs outperform other baselines that relax constraints or use problem-specific architectures.

Even when they predict the wrong labels, they still form a valid configuration!

Join Kareem Ahmed, Stefano Teso, Kai-wei Chang, @guy
and me at
#NeurIPS2022 to talk about #SPLs and how to have #neural #nets to behave in the way we #expect them to do!

📜https://openreview.net/forum?id=o-mxIWAY1T8
🖥️https://github.com/KareemYousrii/SPL

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Our #Semantic #Probabilistic #Layers #SPLs instead always guarantee 100% of the times that predictions satisfy the injected constraints!

They can be readily used in deep nets as they can be trained by #backprop and #maximum #likelihood #estimation.

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