[15] F. Draxler, K. Veschgini, M. Salmhofer, and F. Hamprecht. Essentially no barriers in neural network energy landscape. In International Conference on Machine Learning (ICML), 2018. https://arxiv.org/abs/1803.00885.
[16] R. Entezari, H. Sedghi, O. Saukh, and B. Neyshabur. The role of permutation invariance in linear mode connectivity of neural networks. arXiv preprint arXiv:2110.06296, 2021.
[17] C. Fifty, E. Amid, Z. Zhao, T. Yu, R. Anil, and C. Finn. Efficiently identifying task groupings for multi-task learning. Advances in Neural Information Processing Systems, 34:27503-27516, 2021.
[18] R. A. Fisher. On the mathematical foundations of theoretical statistics. Philosophical transactions of the Royal Society of London. Series A, containing papers of a mathematical or physical character, 222(594-604):309-368, 1922.
[19] J. Frankle, G. K. Dziugaite, D. Roy, and M. Carbin. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning (ICML), 2020. https://proceedings.mlr.press/vl19/frankle20a.html.
[20] C. D. Freeman and J. Bruna. Topology and geometry of half-rectified network optimization. arXiv preprint arXiv:1611.01540, 2016.
[21] T. Garipov, P. Izmailov, D. Podoprikhin, D. Vetrov, and A. G. Wilson. Loss surfaces, mode connectivity, and fast ensembling of dnns. In Advances in Neural Information Processing Systems (NeurIPS), 2018. https://arxiv.org/abs/1802.10026.
[22] A. Gueta, E. Venezian, C. Raffel, N. Slonim, Y. Katz, and L. Choshen. Knowledge is a region in weight space for fine-tuned language models. arXiv preprint arXiv:2302.04863, 2023.
[23] V. Gupta, S. A. Serrano, and D. DeCoste. Stochastic weight averaging in parallel: Large-batch training that generalizes well. International Conference on Learning Representations, 2020.
[24] P. Helber, B. Bischke, A. Dengel, and D. Borth. Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019. https://arxiv.org/abs/1709.00029.
[25] T. Hoefler, D. Alistarh, T. Ben-Nun, N. Dryden, and A. Peste. Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks. The Journal of Machine Learning Research, 22(1):10882-11005, 2021.
[26] E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, and W. Chen. LoRA: Low-rank adaptation of large language models. ArXiv, abs/2106.09685, 2021.
[27] L. Huang, R. Le Bras, C. Bhagavatula, and Y. Choi. Cosmos qa: Machine reading comprehension with contextual commonsense reasoning. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2391–2401, 2019.
[28] G. Ilharco, M. Wortsman, S. Y. Gadre, S. Song, H. Hajishirzi, S. Kornblith, A. Farhadi, and L. Schmidt. Patching open-vocabulary models by interpolating weights. In Advances in Neural Information Processing Systems (NeurIPS), 2022. https://arXiv.org/abs/2208.05592.
[29] G. Ilharco, M. T. Ribeiro, M. Wortsman, L. Schmidt, H. Hajishirzi, and A. Farhadi. Editing models with task arithmetic. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=6t0Kwf8-jrj.
[30] P. Izmailov, D. Podoprikhin, T. Garipov, D. Vetrov, and A. G. Wilson. Averaging weights leads to wider optima and better generalization. In Conference on Uncertainty in Artificial Intelligence (UAI), 2018. https://arxiv.org/abs/1803.05407.
[31] X. Jin, X. Ren, D. Preotiuc-Pietro, and P. Cheng. Dataless knowledge fusion by merging weights of language models. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=FCnohuR6AnM.