Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Holm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N. (2020). The era5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730):1999-2049.
Jaitly, N., Sussillo, D., Le, Q. V., Vinyls, O., Sutskever, I., and Bengio, S. (2015). A neural transducer. arXiv preprint arXiv:1511.04868.
Jin, P., Lu, L., Tang, Y., and Karniadakis, G. E. (2020). Quantifying the generalization error in deep learning in terms of data distribution and neural network smoothness. *Neural Networks*, 130:85–99.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Kirsch, L., Harrison, J., Sohl-Dickstein, J., and Metz, L. (2022). General-purpose in-context learning by meta-learning transformers. In Sixth Workshop on Meta-Learning at the Conference on Neural Information Processing Systems.
Kirsch, L., Schmidhuber, J., and Al (2021). Meta learning backpropagation and improving it. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages 14122-14134. Curran Associates, Inc.
Kissas, G., Seidman, J. H., Guilhoto, L. F., Preciado, V. M., Pappas, G. J., and Perdikaris, P. (2022). Learning operators with coupled attention. Journal of Machine Learning Research, 23(215):1-63.
Laskin, M., Wang, L., Oh, J., Parisotto, E., Spencer, S., Steigerwald, R., Strouse, D., Hansen, S., Filos, A., Brooks, E., Gazeau, M., Sahni, H., Singh, S., and Mnih, V. (2022). In-context reinforcement learning with algorithm distillation.
LeCun, Y. and Cortes, C. (2010). MNIST handwritten digit database.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A. (2020a). Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895.
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Stuart, A., Bhattacharya, K., and Anandkumar, A. (2020b). Multipole graph neural operator for parametric partial differential equations. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H., editors, Advances in Neural Information Processing Systems, volume 33, pages 6755-6766. Curran Associates, Inc.
Lin, R., Zhang, H., and Zhang, J. (2019). On reproducing kernel banach spaces: Generic definitions and unified framework of constructions.
Lin, R. R., Zhang, H. Z., and Zhang, J. (2022). On reproducing kernel banach spaces: Generic definitions and unified framework of constructions. Acta Mathematica Sinica, English Series, 38(8):1459-1483.
Lu, L., Jin, P., and Karniadakis, G. E. (2019). Deeponet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators. arXiv preprint arXiv:1910.03193.
Lu, L., Jin, P., Pang, G., Zhang, Z., and Karniadakis, G. E. (2021). Learning nonlinear operators via deeponet based on the universal approximation theorem of operators. Nature Machine Intelligence, 3(3):218-229.
McCloskey, M. and Cohen, N. J. (1989). Catastrophic interference in connectionist networks: The sequential learning problem. volume 24 of *Psychology of Learning and Motivation*, pages 109-165. Academic Press.