[74] Marco Tulio Ribeiro and Scott Lundberg. Adaptive testing and debugging of nlp models. In Annual Meeting of the Association for Computational Linguistics (ACL), 2022. https://aclanthology.org/2022.acl-long.230/. (page 1).
[75] Basri Ronen, David Jacobs, Yoni Kasten, and Shira Kritchman. The convergence rate of neural networks for learned functions of different frequencies. In Advances in Neural Information Processing Systems (NeurIPS), 2019. https://proceedings.neurips.cc/paper/2019/file/5ac8bb8a7d745102a978c5f8ccdb61b8-Paper.pdf. (page 20).
[76] Shibani Santurkar, Dimitris Tsipras, Mahalaxmi Elango, David Bau, Antonio Torralba, and Aleksander Madry. Editing a classifier by rewriting its prediction rules. In Advances in Neural Information Processing Systems (NeurIPS), 2021. https://proceedings.neurips.cc/paper_files/paper/2021/file/c46489a2d5a9a9ecfc53b17610926ddd-Paper.pdf. (page 1).
[77] Bernhard Schölkopf and Alexander Johannes Smola. Learning with Kernels: support vector machines, regularization, optimization, and beyond. Adaptive computation and machine learning series. MIT Press, 2002. https://mitpress.mit.edu/9780262536578/learning-with-kernels/. (page 8).
[78] Christoph Schuhmann, Richard Vencu, Romain Beaumont, Robert Kaczmarczyk, Clayton Mullis, Aarush Katta, Theo Coombes, Jenia Jitsev, and Aran Komatsuzaki. Laion-400m: Open dataset of clip-filtered 400 million image-text pairs, 2021. https://arxiv.org/abs/2111.02114. (page 25).
[79] Sidak Pal Singh and Martin Jaggi. Model fusion via optimal transport. In Advances in Neural Information Processing Systems (NeurIPS), 2020. https://proceedings.neurips.cc/paper/2020/hash/fb2697869f56484404c8cee2985b01d-Abstract.html. (pages 1 and 9).
[80] Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. The german traffic sign recognition benchmark: a multi-class classification competition. In International Joint Conference on Neural Networks (IJCNN), 2011. https://ieeexplore.ieee.org/document/6033395. (page 4).
[81] Matthew Tancik, Pratul P. Srinivasan, Ben Mildenhall, Sara Fridovich-Keil, Nithin Raghavan, Utkarsh Singhal, Ravi Ramamoorthi, Jonathan T. Barron, and Ren Ng. Fourier features let networks learn high frequency functions in low dimensional domains. In Advances in Neural Information Processing Systems (NeurIPS), 2020. https://proceedings.neurips.cc/paper/2020/file/55053683268957697aa39fba6f231c68-Paper.pdf. (page 9).
[82] Nikhil Vyas, Yamini Bansal, and Preetum Nakkiran. Limitations of the NTK for understanding generalization in deep learning, 2022. https://arxiv.org/abs/2206.10012. (page 9).
[83] Yeming Wen, Dustin Tran, and Jimmy Ba. Batchsemble: an alternative approach to efficient ensemble and lifelong learning. In International Conference on Learning Representations (ICLR), 2020. https://arxiv.org/abs/2002.06715. (page 9).
[84] Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumont, Clement Delangue, Anthony Moi, Pierrick Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, et al. Huggingface's transformers: State-of-the-art natural language processing, 2019. https://arxiv.org/abs/1910.03771. (page 25).
[85] Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi. Supermasks in superposition. In Advances in Neural Information Processing Systems (NeurIPS), 2020. https://proceedings.neurips.cc/paper/2020/file/ad1f8bb9b51f023cdc80cf94bb615aa9-Paper.pdf. (page 9).
[86] Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time. In International Conference on Machine Learning (ICML), 2022. https://arxiv.org/abs/2203.05482. (pages 1, 3, 8, and 9).