PhD Thesis : Arnold, L. (2013). Learning Deep Representations: Toward a better understanding of the deep learning paradigm. [pdf,HAL]
article : Arnold, L., & Ollivier, Y. (2012). Layer-wise learning of deep generative models. 1–46. [pdf, arXiv, HAL]
article : Ollivier, Y., Arnold, L., Auger, A., & Hansen, N. (2017). Information-Geometric Optimization Algorithms: A Unifying Picture via Invariance Principles. Journal of Machine Learning Research, 18, 1–65. [pdf, arXiv, HAL, code]
article : Arnold, L., Rebecchi, S., Chevallier, S., & Hélène, P.-M. (2011). An Introduction to Deep Learning. ESANN 2011. [pdf, HAL]
article : Arnold, L., Paugam-Moisy, H., & Sebag, M. (2010). Unsupervised layer-wise model selection in Deep Neural Networks. Frontiers in Artificial Intelligence and Applications, 215, 915–920. [pdf, HAL]
article : Arnold, L., Paugam-Moisy, H., & Sebag, M. (2010). Optimisation de la Topologie pour les Réseaux de Neurones Profonds. 17e Congrès Francophone AFRIF-AFIA Reconnaissance Des Formes et Intelligence Artificielle – RFIA 2010. [pdf,HAL]
