• GotenNet: Rethinking Efficient 3D Equivariant Graph Neural Networks

    Sarp Aykent, Tian Xia

    ICLR ,The Thirteenth International Conference on Learning Representations, pp. 40622–40646

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    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}
}

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    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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  • SE(3) Equivariant Neural Network for 3D Graphs

    Sarp Aykent, Tian Xia

    BigData ,IEEE International Conference on Big Data, pp. 1716–1725

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

    Cite SE(3) Equivariant Neural Network for 3D Graphs

    BibTeX

    @inproceedings{aykent2024se3,
  author = {Aykent, Sarp and Xia, Tian},
  title = {{SE(3) Equivariant Neural Network for 3D Graphs}},
  booktitle = {2024 IEEE International Conference on Big Data},
  pages = {1716--1725},
  year = {2024},
  month = dec,
  organization = {IEEE},
  doi = {10.1109/BigData62323.2024.10825716}
}

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    APA
    Aykent, S., & Xia, T. (2024). SE(3) Equivariant Neural Network for 3D Graphs. 2024 IEEE International Conference on Big Data, 1716–1725. https://doi.org/10.1109/BigData62323.2024.10825716
    Vancouver
    Aykent S, Xia T. SE(3) Equivariant Neural Network for 3D Graphs. In: 2024 IEEE International Conference on Big Data. IEEE; 2024. p. 1716–25. doi:10.1109/BigData62323.2024.10825716
    Harvard
    Aykent, S. and Xia, T. (2024) “SE(3) Equivariant Neural Network for 3D Graphs,” 2024 IEEE International Conference on Big Data. IEEE, pp. 1716–1725. Available at: https://doi.org/10.1109/BigData62323.2024.10825716.
    MLA
    Aykent, Sarp, and Tian Xia. “SE(3) Equivariant Neural Network for 3D Graphs.” 2024 IEEE International Conference on Big Data, 2024, pp. 1716–25, https://doi.org/10.1109/BigData62323.2024.10825716.

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  • 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

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    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}
}

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    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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  • Exploring the Anonymity of Social Media Users Using Micro-messages

    Sarp Aykent, Cheryl Seals

    HCII ,Social Computing and Social Media, vol. 14025, pp. 14–24

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

    Cite Exploring the Anonymity of Social Media Users Using Micro-messages

    BibTeX

    @inproceedings{aykent2023anonymity,
  author = {Aykent, Sarp and Seals, Cheryl},
  title = {{Exploring the Anonymity of Social Media Users Using Micro-messages}},
  booktitle = {Social Computing and Social Media},
  series = {Lecture Notes in Computer Science},
  volume = {14025},
  pages = {14--24},
  year = {2023},
  month = jul,
  publisher = {Springer Nature Switzerland},
  address = {Cham},
  doi = {10.1007/978-3-031-35915-6_2},
  editor = {Coman, Adela and Vasilache, Simona}
}

    Other styles

    APA
    Aykent, S., & Seals, C. (2023). Exploring the Anonymity of Social Media Users Using Micro-messages. In A. Coman & S. Vasilache (Eds.), Social Computing and Social Media (Vol. 14025, pp. 14–24). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-35915-6_2
    Vancouver
    Aykent S, Seals C. Exploring the Anonymity of Social Media Users Using Micro-messages. In: Coman A, Vasilache S, editors. Social Computing and Social Media. Cham: Springer Nature Switzerland; 2023. p. 14–24. (Lecture Notes in Computer Science). doi:10.1007/978-3-031-35915-6_2
    Harvard
    Aykent, S. and Seals, C. (2023) “Exploring the Anonymity of Social Media Users Using Micro-messages,” in A. Coman and S. Vasilache (eds.) Social Computing and Social Media. Cham: Springer Nature Switzerland (Lecture Notes in Computer Science), pp. 14–24. Available at: https://doi.org/10.1007/978-3-031-35915-6_2.
    MLA
    Aykent, Sarp, and Cheryl Seals. “Exploring the Anonymity of Social Media Users Using Micro-Messages.” Social Computing and Social Media, edited by Adela Coman and Simona Vasilache, vol. 14025, Springer Nature Switzerland, 2023, pp. 14–24, https://doi.org/10.1007/978-3-031-35915-6_2. Lecture Notes in Computer Science.

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