References
S. Greydanus, M. Dzamba and J. Yosinski. Hamiltonian Neural Networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems (Curran Associates Inc., Vancouver, 2019); pp. 15379–15389.
H. Luo, H. Wu, H. Zhou, L. Xing, Y. Di, J. Wang and M. Long. Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries (Feb 2025), arXiv:2502.02414.
H. Wu, H. Luo, H. Wang, J. Wang and M. Long. Transolver: A Fast Transformer Solver for PDEs on General Geometries. In: Proceedings of the 41st International Conference on Machine Learning, ICML'24 (JMLR, Vienna, 2024).
N. Gaby, F. Zhang and X. Ye. Lyapunov-Net: A Deep Neural Network Architecture for Lyapunov Function Approximation. In: 2022 IEEE 61st Conference on Decision and Control (CDC) (IEEE, Cancun, Dec 2022); pp. 2091–2096.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit and N. Houlsby. An Image Is Worth 16x16 Words: Transformers for Image Recognition at Scale (Jun 2021), arXiv:2010.11929.
A. Krizhevsky, I. Sutskever and G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM 60, 84–90 (2017).
M. Tan and Q. Le. Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. In: Proceedings of the 36th International Conference on Machine Learning, Vol. 97 (PMLR, PMLR, 2019); pp. 6105–6114.
K. Simonyan and A. Zisserman. Very Deep Convolutional Networks for Large-Scale Image Recognition (Apr 2015), arXiv:1409.1556.
A. Trockman and J. Z. Kolter. Patches Are All You Need? (Jan 2022), arXiv:2201.09792.
G. Huang, Z. Liu, L. Van Der Maaten and K. Q. Weinberger. Densely Connected Convolutional Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Honolulu, Jul 2017); pp. 4700–4708.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke and A. Rabinovich. Going Deeper with Convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Boston, MA, USA, Jun 2015); pp. 1–9.
A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications (Apr 2017), arXiv:1704.04861.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L.-C. Chen. MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Salt Lake City, UT, Jun 2018); pp. 4510–4520.
A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y. Zhu, R. Pang, V. Vasudevan, H. Adam and Q. Le. Searching for MobileNetV3. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (IEEE, Seoul, Oct 2019); pp. 1314–1324.
K. He, X. Zhang, S. Ren and J. Sun. Deep Residual Learning for Image Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Las Vegas, Jun 2016); pp. 770–778.
S. Xie, R. Girshick, P. Dollár, Z. Tu and K. He. Aggregated Residual Transformations for Deep Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, Honolulu, HI, Jul 2017); pp. 1492–1500.
F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally and K. Keutzer. SqueezeNet: AlexNet-level Accuracy with 50x Fewer Parameters and $<$0.5MB Model Size (Nov 2016), arXiv:1602.07360 [cs.CV].
S. Zagoruyko and N. Komodakis. Wide Residual Networks, arXiv:1605.07146 [cs.CV].