[21] Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015. 23
[22] Alexander Immer, Matthias Bauer, Vincent Fortuin, Gunnar Ratsch, and Khan Mohammad Emtiyaz. Scalable marginal likelihood estimation for model selection in deep learning. In International Conference on Machine Learning, pages 4563-4573. PMLR, 2021. 7
[23] Sergey Ioffe and Christian Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Francis Bach and David Blei, editors, Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pages 448-456, Lille, France, 07-09 Jul 2015. PMLR. 20
[24] John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, et al. Highly accurate protein structure prediction with alphafold. Nature, 596(7873):583-589, 2021. 1
[25] Pang Wei Koh and Percy Liang. Understanding black-box predictions via influence functions. In International conference on machine learning, pages 1885-1894. PMLR, 2017. 1, 2, 3, 4, 5, 7, 8, 16
[26] Shuming Kong, Yanyan Shen, and Linpeng Huang. Resolving training biases via influence-based data relabeling. In International Conference on Learning Representations, 2021. 9
[27] A Krizhevsky. Learning multiple layers of features from tiny images. Master's thesis, University of Toronto, 2009. 8, 20
[28] Taku Kudo and John Richardson. Sentencepiece: A simple and language independent subword tokenizer and tokenizer for neural text processing. arXiv preprint arXiv:1808.06226, 2018.20
[29] Brian Kulis, Sugato Basu, Inderjit Dhillon, and Raymond Mooney. Semi-supervised graph clustering: a kernel approach. In Proceedings of the 22nd international conference on machine learning, pages 457-464, 2005. 6
[30] Shinjini Kundu. Ai in medicine must be explainable. Nature medicine, 27(8):1328-1328, 2021. 1
[31] Frederik Kunstner, Philipp Hennig, and Lukas Balles. Limitations of the empirical fisher approximation for natural gradient descent. Advances in neural information processing systems, 32, 2019. 7
[32] Valliappa Lakshmanan, Sara Robinson, and Michael Munn. Machine learning design patterns. O'Reilly Media, 2020. 1
[33] Y LeCun, C Cortes, and C Burges. The mnist dataset of handwritten digits (images). NYU: New York, NY, USA, 1999. 4, 9, 20, 21
[34] Jens Lehmann, Robert Isele, Max Jakob, Anja Jentzsch, Dimitris Kontokostas, Pablo N Mendes, Sebastian Hellmann, Mohamed Morsey, Patrick Van Kleef, Soren Auer, et al. Dbpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web, 6(2):167-195, 2015. 20, 22
[35] Sheng Liu, Jonathan Niles-Weed, Narges Razavian, and Carlos Fernandez-Granda. Early-learning regularization prevents memorization of noisy labels. Advances in neural information processing systems, 33:20331–20342, 2020. 7
[36] Ilya Loshchilov and Frank Hutter. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 20
[37] Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019. 20