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Creators/Authors contains: "Liang, Yitao"

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  1. ; ; ; (, Proceedings of the 40th International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  2. ; ; ; (, Proceedings of the 37th AAAI Conference on Artificial Intelligence)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Proceedings of the 35th AAAI Conference on Artificial Intelligence (Demo Track))
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, Proceedings of the 35th International Conference on Machine Learning (ICML))
    This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output vectors and logical constraints. This loss function captures how close the neural network is to satisfying the constraints on its output. An experimental evaluation shows that it effectively guides the learner to achieve (near-)state-of-the-art results on semi-supervised multi-class classification. Moreover, it significantly increases the ability of the neural network to predict structured objects, such as rankings and paths. These discrete concepts are tremendously difficult to learn, and benefit from a tight integration of deep learning and symbolic reasoning methods. 
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    Accepted Manuscript Full Text Available
  5. ; ; (, Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence (UAI))
    Accepted Manuscript Full Text Available