Understanding Deep Learning

Towards the unification of deep neural networks and iterative proximal methods.

(Coming soon… )

Related Publications

1. D. Pinto-Suarez, J. Patracone and M. Sebban,

   Principled Backpropagation-Free Training via Layerwise Fenchel-Young Lifting,

   preprint, 2026.
   Article Page   PDF   BibTeX  
2. D. Pinto-Suarez, J. Patracone and M. Sebban,

   Beyond Gaussian Initialization -- Activation-Induced Gibbs States for Critical and Width-Efficient Networks,

   preprint, 2026.
   Article Page   PDF   BibTeX  
3. B. Gao, J. Patracone and O. Alata,

   Forecasting under Persistent-Transient Ambiguity,

   preprint, 2026.
   Article Page   PDF   BibTeX  
4. J. Frecon, G. Gasso, M. Pontil and S. Salzo,

   Bregman Neural Networks,

   International Conference on Machine Learning (ICML), 2022.
   Article Page   PDF   BibTeX   Toolbox  

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