[10] BENGIO, Y., LAMBLIN, P., POPOVICI, D., AND LAROCHELLE, H. Greedy layer-wise training of deep networks. In NeurIPS (2006).
[11] BERND ILLING, WULFRAM GERSTNER, G. B. Towards truly local gradients with clapp: Contrastive, local and predictive plasticity. arXiv (2020).
[12] COATE, A., LEE, H., AND NG, A. Y. An analysis of single layer networks in unsupervised feature learning. In AISTATS (2011).
[13] CYBENKO, G. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems 2 (1989), 303-314.
[14] CZARNECKI, W. M., SWIRSZCZ, G., JADERBERG, M., OSINDERO, S., VINYALS, O., AND KAVUKCUOGLU, K. Understanding synthetic gradients and decoupled neural interfaces. In ICML (2017).
[15] DE, S., AND SMITH, S. L. Batch normalization biases residual blocks towards the identity function in deep networks. In NeurIPS (2020).
[16] DE BÉZENAC, E., AYED, I., AND GALLINARI, P. Optimal unsupervised domain translation. arXiv (2019).
[17] DOSOVITSKIY, A., BEYER, L., KOLESNIKOV, A., WEISSENBORN, D., ZHAI, X., UNTERTHINER, T., DEHGHANI, M., MINDERER, M., HEIGOLD, G., GELLY, S., USZKOREIT, J., AND HOULSBY, N. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations (2021).
[18] DU, S. S., AND GOEL, S. Improved learning of one-hidden-layer convolutional neural networks with overlaps. arXiv (2018).
[19] DUN, C., WOLFE, C. R., JERMAINE, C. M., AND KYRILLIDIS, A. Resist: Layer-wise decomposition of resnets for distributed training. arXiv (2021).
[20] FIGALLI, A. The Monge-Ampere Equation and Its Applications. Zurich lectures in advanced mathematics. European Mathematical Society, 2017.
[21] GAO, Y., JIAO, Y., WANG, Y., WANG, Y., YANG, C., AND ZHANG, S. Deep generative learning via variational gradient flow. In ICML (2019).
[22] GE, R., LEE, J. D., AND MA, T. Learning one-hidden-layer neural networks with landscape design. In International Conference on Learning Representations (2018).
[23] GIANAZZA, U., GOBBINO, M., AND SAVARE, G. Evolution problems and minimizing movements. Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti Lincei. Matematica e Applicazioni 5 (1994), 289-296.
[24] GIORGI, E. D., MARINO, A., AND TOSQUES, M. Problemi di evoluzione in spazi metrici e curve di massima pendenza. Atti della Accademia Nazionale dei Lincei. Classe di Scienze Fisiche, Matematiche e Naturali. Rendiconti 68, 3 (3 1980), 180-187.
[25] GUPTA, S. K. A more biologically plausible local learning rule for anns. arXiv (2020).
[26] HAUSER, M. On residual networks learning a perturbation from identity. arXiv (2019).
[27] HE, K., ZHAN, X., REN, S., AND SUN, J. Identity mappings in deep residual networks. In ECCV (2016).
[28] HE, K., ZHANG, X., REN, S., AND SUN, J. Deep residual learning for image recognition. In CVPR (2016).
[29] HORNIK, K., STINCHCOMBE, M., AND WHITE, H. Multilayer feedforward networks are universal approximators. Neural Networks 2, 5 (1989), 359-366.
[30] HUANG, F., ASH, J., LANGFORD, J., AND SCHAPIRE, R. Learning deep resnet blocks sequentially using boosting theory. In ICML (2018).
[31] HUO, Z., GU, B., AND HUANG, H. Training neural networks using features replay. In NeurIPS (2018).
[32] HUO, Z., GU, B., YANG, Q., AND HUANG, H. Decoupled parallel backpropagation with convergence guarantee. In ICML (2018).
[33] JADERBERG, M., CZARNECKI, W. M., OSINDERO, S., VINYALS, O., GRAVES, A., SILVER, D., AND KAVUKCUOGLU, K. Decoupled neural interfaces using synthetic gradients. In ICML (2017).