[37] A. Luo, Y. Du, M. J. Tarr, J. B. Tenenbaum, A. Torralba, and C. Gan. Learning neural acoustic fields. In Advances in Neural Information Processing Systems, 2022.
[38] J. N. Martel, D. B. Lindell, C. Z. Lin, E. R. Chan, M. Monteiro, and G. Wetzstein. Acorn: Adaptive coordinate networks for neural scene representation. ACM Trans. Graph. (SIGGRAPH), 2021.
[39] L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger. Occupancy networks: Learning 3D reconstruction in function space. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[40] B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamorthi, and R. Ng. NeRF: Representing scenes as neural radiance fields for view synthesis. In European Conference on Computer Vision, 2020.
[41] A. Nichol, J. Achiam, and J. Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.
[42] V. Panayotov, G. Chen, D. Povey, and S. Khudanpur. Librispeech: an asr corpus based on public domain audio books. In IEEE International Conference on Acoustics, Speech and Signal Processing, 2015.
[43] J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove. DeepSDF: Learning continuous signed distance functions for shape representation. In IEEE Conference on Computer Vision and Pattern Recognition, 2019.
[44] M. Paul, S. Ganguli, and G. K. Dziugaite. Deep learning on a data diet: Finding important examples early in training. In Advances in Neural Information Processing Systems, 2021.
[45] A. Rajeswaran, C. Finn, S. M. Kakade, and S. Levine. Meta-learning with implicit gradients. Advances in Neural Information Processing Systems, 2019.
[46] M. Ren, W. Zeng, B. Yang, and R. Urtasun. Learning to reweight examples for robust deep learning. In International Conference on Machine Learning, 2018.
[47] D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne. Experience replay for continual learning. In Advances in Neural Information Processing Systems, 2019.
[48] J. R. Schwarz and Y. W. Teh. Meta-learning sparse compression networks. Transactions on Machine Learning Research, 2022.
[49] J. R. Schwarz, J. Tack, Y. W. Teh, J. Lee, and J. Shin. Modality-agnostic variational compression of implicit neural representations. In International Conference on Machine Learning, 2023.
[50] O. Sener and S. Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018.
[51] J. Shin, H. B. Lee, B. Gong, and S. J. Hwang. Large-scale meta-learning with continual trajectory shifting. In International Conference on Machine Learning, 2021.
[52] V. Sitzmann, M. Zollhöfer, and G. Wetzstein. Scene representation networks: Continuous 3D-structure-aware neural scene representations. In Advances in Neural Information Processing Systems, 2019.
[53] V. Sitzmann, E. R. Chan, R. Tucker, N. Snavely, and G. Wetzstein. MetaSDF: Meta-learning signed distance functions. In Advances in Neural Information Processing Systems, 2020.
[54] V. Sitzmann, J. N. P. Martel, A. W. Bergman, D. B. Lindell, and G. Wetzstein. Implicit neural representations with periodic activation functions. In Advances in Neural Information Processing Systems, 2020.
[55] I. Skorokhodov, S. Ignatyev, and M. Elhoseiny. Adversarial generation of continuous images. In IEEE Conference on Computer Vision and Pattern Recognition, 2021.