Cite GotenNet

BibTeX

@inproceedings{aykent2025gotennet,
  author = {Aykent, Sarp and Xia, Tian},
  title = {{GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks}},
  booktitle = {The Thirteenth International Conference on Learning Representations},
  pages = {40622--40646},
  year = {2025},
  url = {https://openreview.net/forum?id=5wxCQDtbMo}
}

Other styles

APA
Aykent, S., & Xia, T. (2025). GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks. The Thirteenth International Conference on Learning Representations, 40622–40646. https://openreview.net/forum?id=5wxCQDtbMo
Vancouver
Aykent S, Xia T. GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks. In: The Thirteenth International Conference on Learning Representations [Internet]. 2025. p. 40622–46. Available from: https://openreview.net/forum?id=5wxCQDtbMo
Harvard
Aykent, S. and Xia, T. (2025) “GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks,” The Thirteenth International Conference on Learning Representations, pp. 40622–40646. Available at: https://openreview.net/forum?id=5wxCQDtbMo.
MLA
Aykent, Sarp, and Tian Xia. “GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks.” The Thirteenth International Conference on Learning Representations, 2025, pp. 40622–46, https://openreview.net/forum?id=5wxCQDtbMo.

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More recent publications

All publications

  • SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning

    Sarp Aykent, Tian Xia

    NeurIPS ,Thirty-seventh Conference on Neural Information Processing Systems, vol. 36, pp. 42932–42949, 26.1% acceptance

    PDF (opens in new tab) Code (opens in new tab)

    Cite SaVeNet

    BibTeX

    @inproceedings{aykent2023savenet,
  author = {Aykent, Sarp and Xia, Tian},
  title = {{SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning}},
  booktitle = {Thirty-seventh Conference on Neural Information Processing Systems},
  volume = {36},
  pages = {42932--42949},
  year = {2023},
  month = dec,
  publisher = {Curran Associates, Inc.},
  doi = {10.52202/075280-1860},
  url = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/860c1c657deafe09f64c013c2888bd7b-Abstract-Conference.html}
}

    Other styles

    APA
    Aykent, S., & Xia, T. (2023). SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning. Thirty-Seventh Conference on Neural Information Processing Systems, 36, 42932–42949. https://doi.org/10.52202/075280-1860
    Vancouver
    Aykent S, Xia T. SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning. In: Thirty-seventh Conference on Neural Information Processing Systems [Internet]. Curran Associates, Inc.; 2023. p. 42932–49. Available from: https://proceedings.neurips.cc/paper_files/paper/2023/hash/860c1c657deafe09f64c013c2888bd7b-Abstract-Conference.html doi:10.52202/075280-1860
    Harvard
    Aykent, S. and Xia, T. (2023) “SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning,” Thirty-seventh Conference on Neural Information Processing Systems. Curran Associates, Inc., pp. 42932–42949. Available at: https://doi.org/10.52202/075280-1860.
    MLA
    Aykent, Sarp, and Tian Xia. “SaVeNet: A Scalable Vector Network for Enhanced Molecular Representation Learning.” Thirty-Seventh Conference on Neural Information Processing Systems, vol. 36, 2023, pp. 42932–49, https://doi.org/10.52202/075280-1860.

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  • GBPNet: Universal Geometric Representation Learning on Protein Structures

    Sarp Aykent, Tian Xia

    KDD ,Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4–14, 14.99% acceptance

    DOI (publisher's page, opens in new tab) Code (opens in new tab)

    Cite GBPNet

    BibTeX

    @inproceedings{gbp2022,
  author = {Aykent, Sarp and Xia, Tian},
  title = {{GBPNet: Universal Geometric Representation Learning on Protein Structures}},
  booktitle = {Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
  series = {KDD '22},
  pages = {4--14},
  year = {2022},
  month = aug,
  publisher = {Association for Computing Machinery},
  address = {New York, NY, USA},
  isbn = {9781450393850},
  doi = {10.1145/3534678.3539441},
  venue = {Washington, DC, USA}
}

    Other styles

    APA
    Aykent, S., & Xia, T. (2022). GBPNet: Universal Geometric Representation Learning on Protein Structures. Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD ’22, 4–14. https://doi.org/10.1145/3534678.3539441
    Vancouver
    Aykent S, Xia T. GBPNet: Universal Geometric Representation Learning on Protein Structures. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery; 2022. p. 4–14. (KDD ’22). doi:10.1145/3534678.3539441
    Harvard
    Aykent, S. and Xia, T. (2022) “GBPNet: Universal Geometric Representation Learning on Protein Structures,” Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: Association for Computing Machinery (KDD ’22), pp. 4–14. Available at: https://doi.org/10.1145/3534678.3539441.
    MLA
    Aykent, Sarp, and Tian Xia. “GBPNet: Universal Geometric Representation Learning on Protein Structures.” Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining [New York, NY, USA], KDD ’22, 2022, pp. 4–14, https://doi.org/10.1145/3534678.3539441.

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