20022022

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  • Conference contribution

    More data means less inference: A pseudo-max approach to structured learning

    Sontag, D., Meshi, O., Jaakkola, T. & Globerson, A., 2010, Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010. (Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Nightmare at test time: Robust learning by feature deletion

    Globerson, A. & Roweis, S., 2006, ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning. p. 353-360 8 p. (ICML 2006 - Proceedings of the 23rd International Conference on Machine Learning; vol. 2006).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Nightmare at test time: Robust learning by feature deletion

    Globerson, A. & Roweis, S., 2006, ACM International Conference Proceeding Series - Proceedings of the 23rd International Conference on Machine Learning, ICML 2006. p. 353-360 8 p. (ACM International Conference Proceeding Series; vol. 148).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Object-Region Video Transformers

    Herzig, R., Ben-Avraham, E., Mangalam, K., Bar, A., Chechik, G., Rohrbach, A., Darrell, T. & Globerson, A., 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. IEEE Computer Society, p. 3138-3149 12 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • On the Inductive Bias of Neural Networks for Learning Read-once DNFs

    Bronstein, I., Brutzkus, A. & Globerson, A., 2022, Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022. Association For Uncertainty in Artificial Intelligence (AUAI), p. 255-265 11 p. (Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Predict and constrain: Modeling cardinality in deep structured prediction

    Brukhim, N. & Globerson, A., 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 1046-1054 9 p. (35th International Conference on Machine Learning, ICML 2018; vol. 2).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Pre-training mention representations in coreference models

    Varkel, Y. & Globerson, A., 2020, EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics (ACL), p. 8534-8540 7 p. (EMNLP 2020 - 2020 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Selective sharing for multilingual dependency parsing

    Naseem, T., Barzilay, R. & Globerson, A., 2012, 50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference. p. 629-637 9 p. (50th Annual Meeting of the Association for Computational Linguistics, ACL 2012 - Proceedings of the Conference; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Spatio-temporal action graph networks

    Herzig, R., Levi, E., Xu, H., Gao, H., Brosh, E., Wang, X., Globerson, A. & Darrell, T., Oct 2019, Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. Institute of Electrical and Electronics Engineers Inc., p. 2347-2356 10 p. 9022086. (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Spectral regularization for max-margin sequence tagging

    Quattoni, A., Balle, B., Carreras, X. & Globerson, A., 2014, 31st International Conference on Machine Learning, ICML 2014. International Machine Learning Society (IMLS), p. 3698-3706 9 p. (31st International Conference on Machine Learning, ICML 2014; vol. 5).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Steps to excellence: Simple inference with refined scoring of dependency trees

    Zhang, Y., Lei, T., Barzilay, R., Jaakkola, T. & Globerson, A., 2014, Long Papers. Association for Computational Linguistics (ACL), p. 197-207 11 p. (52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Template kernels for dependency parsing

    Taub-Tabib, H., Goldberg, Y. & Globerson, A., 2015, NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference. Association for Computational Linguistics (ACL), p. 1422-1427 6 p. (NAACL HLT 2015 - 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Tightening LP relaxations for MAP using message passing

    Sontag, D., Meltzer, T., Globerson, A., Jaakkola, T. & Weiss, Y., 2008, Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008. p. 503-510 8 p. (Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, UAI 2008).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Tightness results for local consistency relaxations in continuous MRFs

    Wald, Y. & Globerson, A., 2014, Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014. Zhang, N. L. & Tian, J. (eds.). AUAI Press, p. 839-848 10 p. (Uncertainty in Artificial Intelligence - Proceedings of the 30th Conference, UAI 2014).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Transfer learning for constituency-based grammars

    Zhang, Y., Barzilay, R. & Globerson, A., 2013, Long Papers. Association for Computational Linguistics (ACL), p. 291-301 11 p. (ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Weakly supervised semantic parsing with abstract examples

    Goldman, O., Latcinnik, V., Naveh, U., Globerson, A. & Berant, J., 2018, ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers). Association for Computational Linguistics (ACL), p. 1809-1819 11 p. (ACL 2018 - 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers); vol. 1).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    Open Access
  • What cannot be learned with bethe approximations

    Heinemann, U. & Globerson, A., 2011, Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011. AUAI Press, p. 319-326 8 p. (Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, UAI 2011).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

  • Why do larger models generalize better? A theoretical perspective via the XOR problem

    Brutzkus, A. & Globerson, A., 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 1310-1318 9 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review