Banburski, A. et al. Dynamics & Generalization in Deep Networks -Minimizing the Norm. NAS Sackler Colloquium on Science of Deep Learning (2019).

Banburski, A. et al. Weight and Batch Normalization implement Classical Generalization Bounds . ICML (2019).

Liao, Q., Miranda, B., Hidary, J. & Poggio, T. Classical generalization bounds are surprisingly tight for Deep Networks. (2018).

Banburski, A. et al. Theory III: Dynamics and Generalization in Deep Networks. (2018).

Zhang, C. et al. Musings on Deep Learning: Properties of SGD. (2017).

Zhang, C. et al. Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).

Poggio, T. et al. Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2

Poggio, T., Mhaskar, H., Rosasco, L., Miranda, B. & Liao, Q. Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016).