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10506 Publications

2024 | Published | Conference Paper | IST-REx-ID: 18113 |

V. Egiazarian, A. Panferov, D. Kuznedelev, E. Frantar, A. Babenko, and D.-A. Alistarh, “Extreme compression of large language models via additive quantization,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12284–12303.

[Preprint] View | Download Preprint (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18114 |

A. A. Pervez, F. Locatello, and E. Gavves, “Mechanistic neural networks for scientific machine learning,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 40484–40501.

[Published Version] View | Files available | Download Published Version (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18115 |

K. Axiotis et al., “Data-efficient learning via clustering-based sensitivity sampling: Foundation models and beyond,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 2086–2107.

[Published Version] View | Download Published Version (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18116 |

M. D. La Tour, M. Henzinger, and D. Saulpic, “Making old things new: A unified algorithm for differentially private clustering,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 12046–12086.

[Published Version] View | Download Published Version (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18117 |

M. Nikdan, S. Tabesh, E. Crncevic, and D.-A. Alistarh, “RoSA: Accurate parameter-efficient fine-tuning via robust adaptation,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 38187–38206.

[Preprint] View | Files available | Download Preprint (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18118 |

H. Zakerinia, A. Behjati, and C. Lampert, “More flexible PAC-Bayesian meta-learning by learning learning algorithms,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 58122–58139.

[Published Version] View | Download Published Version (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18120 |

J. A. Scott and Á. Cahill, “Improved modelling of federated datasets using mixtures-of-Dirichlet-multinomials,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 44012–44037.

[Preprint] View | Download Preprint (ext.) | arXiv

2024 | Published | Conference Paper | IST-REx-ID: 18121 |

A. S. Moakhar, E. B. Iofinova, E. Frantar, and D.-A. Alistarh, “SPADE: Sparsity-guided debugging for deep neural networks,” in Proceedings of the 41st International Conference on Machine Learning, Vienna, Austria, 2024, vol. 235, pp. 45955–45987.

[Preprint] View | Files available | Download Preprint (ext.) | arXiv

2024 | Published | Journal Article | IST-REx-ID: 18153 |

M. A. Hajnal et al., “Shifts in attention drive context-dependent subspace encoding in anterior cingulate cortex in mice during decision making,” Nature Communications, vol. 15. Springer Nature, 2024.

[Published Version] View | Files available | DOI | PubMed | Europe PMC

2024 | Published | Conference Paper | IST-REx-ID: 18155 |

K. Chatterjee, A. K. Goharshady, E. Goharshady, M. Karrabi, and D. Zikelic, “Sound and complete witnesses for template-based verification of LTL properties on polynomial programs,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Milan, Italy, 2024, vol. 14933, pp. 600–619.