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Award ID contains: 1633857

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  1. (, Proceedings of the 36th Conference on Uncertainty in Aritifical Intelligence (UAI))
    null (Ed.)
    Accepted Manuscript Full Text Available
  2. (, Proceedings of the 37th International Conference on Machine Learning (ICML))
    null (Ed.)
    Accepted Manuscript Full Text Available
  3. ; (, Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI))
    Accepted Manuscript Full Text Available
  4. (, Advances in Neural Information Processing Systems 32 (NeurIPS))
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. (, The 1st Conference On Automated Knowledge Base Construction (AKBC))
    Accepted Manuscript Full Text Available
  6. (, The 41st ACM/IEEE International Conference on Software Engineering (ICSE))
    Accepted Manuscript Full Text Available
  7. (, Advances in Neural Information Processing Systems 32 (NeurIPS))
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; ; ; (, 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
  9. ; ; (, Journal of Applied Logic)
    Full Text Available 
  10. ; ; ; (, Advances in Neural Information Processing Systems 29 (NIPS))
    Accepted Manuscript Full Text Available