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Emile van Krieken

MR.

 https://orcid.org/0000-0001-5502-4817

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Biography

I am a post-doc in the NLP group under the ELIAI program. My research combines symbolic reasoning and machine learning, or “Neurosymbolic Learning”. I’ve done work on differentiable fuzzy logics and optimization with discrete latent variables. I developed the Storchastic PyTorch library, which implements many gradient estimation methods. I recently developed A-NeSI, a highly scalable Neurosymbolic method that uses neural networks for symbolic inference. At Edinburgh, I am continuing to work on neural networks with discrete latent variables.

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  • 4 Conference contribution
  • 3 Article

  • Refining neural network predictions using background knowledge

    Daniele, A., van Krieken, E., Serafini, L. & van Harmelen, F., 14 Mar 2023, In: Machine Learning. 112, 9, p. 3293-3331 39 p.

    Research output: Contribution to journal › Article › peer-review

    Open Access
    File

  • Prompting as Probing: Using Language Models for Knowledge Base Construction

    Alivanistos, D., Santamaría, S. B., Cochez, M., Kalo, J. C., van Krieken, E. & Thanapalasingam, T., 16 Nov 2022, LM-KBC 2022 Knowledge Base Construction from Pre-trained Language Models 2022: Proceedings of the Semantic Web Challenge on Knowledge Base Construction from Pre-trained Language Models 2022 co-located with the 21st International Semantic Web Conference (ISWC2022). Singhania, S., Nguyen, T-P. & Razniewski, S. (eds.). CEUR-WS.org, Vol. 3274. p. 11-34 24 p. (CEUR Workshop Proceedings).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    Open Access
    File

  • Storchastic: A Framework for General Stochastic Automatic Differentiation

    van Krieken, E., Tomczak, J. M. & ten Teije, A., 1 Dec 2021, Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural Information Processing Systems Foundation, Inc, p. 7574-7587 14 p. (Advances in Neural Information Processing Systems).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    Open Access

  • Analyzing Differentiable Fuzzy Logic Operators

    van Krieken, E., Acar, E. & van Harmelen, F., 13 Oct 2021, In: Artificial Intelligence. 302, p. 1-46 46 p., 103602.

    Research output: Contribution to journal › Article › peer-review

    Open Access
    File

  • Analyzing Differentiable Fuzzy Implications

    Krieken, E. V., Acar, E. & Harmelen, F. V., 18 Sept 2020, Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR 2020) Special Session on KR and Machine Learning. Calvanese, D., Erdem, E. & Thielscher, M. (eds.). IJCAI Organization, p. 893-903 11 p. (KR Proceedings: Conference on Principles of Knowledge Representation and Reasoning).

    Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

    Open Access
View all 7 research outputs