G. Aupy and J. Herrmann, H-Revolve: A Framework for Adjoint Computation on Synchrone Hierarchical Platforms. working paper or preprint, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02080706

G. Aupy, J. Herrmann, P. Hovland, and Y. Robert, Optimal multistage algorithm for adjoint computation, SIAM Journal on Scientific Computing, vol.38, issue.3, pp.232-255, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01147155

J. Bgl-+-94, I. Bromley, Y. Guyon, E. Lecun, R. Säckinger et al., Signature verification using a" siamese" time delay neural network, Advances in neural information processing systems, pp.737-744, 1994.

O. Beaumont, J. Herrmann, G. Pallez, and A. Shilova, Optimal Memory-aware Backpropagation of Deep Join Networks, INRIA, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02401105

T. Chen, B. Xu, C. Zhang, and C. Guestrin, Training deep nets with sublinear memory cost, 2016.

. Dam-+-16-dipankar, S. Das, D. Avancha, K. Mudigere, S. Vaidynathan et al., Distributed deep learning using synchronous stochastic gradient descent, 2016.

G. Dcm-+-12-jeffrey-dean, R. Corrado, K. Monga, M. Chen, M. Devin et al., Understanding the difficulty of training deep feedforward neural networks, DFS17 William Du, Michael Fang, and Margaret Shen. Siamese convolutional neural networks for authorship verification. Proceedings, 2017. GB10 Xavier Glorot and Yoshua Bengio, pp.249-256, 2010.

R. Gmd-+-16-audrunas-gruslys, I. Munos, M. Danihelka, A. Lanctot, and . Graves, Algorithm 799: Revolve: an implementation of checkpointing for the reverse or adjoint mode of computational differentiation, International Workshop on Similarity-Based Pattern Recognition, vol.6, pp.84-92, 1989.

E. Kayaaslan, T. Lambert, L. Marchal, and B. Liu, An application of generalized tree pebbling to sparse matrix factorization, SIAM Journal on Algebraic Discrete Methods, vol.707, issue.3, pp.375-395, 1987.

M. Mab-+-19, A. Mueller, S. Arzt, M. Balke, G. Dorfer et al., Cross-modal music retrieval and applications: An overview of key methodologies, IEEE Signal Processing Magazine, vol.36, issue.1, pp.52-62, 2019.

J. Mbo-+-18, A. Marin, F. Biswas, N. Ofli, A. Hynes et al., Recipe1m: A dataset for learning cross-modal embeddings for cooking recipes and food images, 2018.

J. Masci, D. Migliore, J. Michael-m-bronstein, and . Schmidhuber, Descriptor learning for omnidirectional image matching, Registration and Recognition in Images and Videos, pp.49-62, 2014.

S. Pgc-+-17-adam-paszke, S. Gross, G. Chintala, E. Chanan, Z. Yang et al., Automatic differentiation in pytorch, 2017.

N. Rgc-+-16-minsoo-rhu, J. Gimelshein, A. Clemons, S. W. Zulfiqar, and . Keckler, vdnn: Virtualized deep neural networks for scalable, memory-efficient neural network design, The 49th Annual IEEE/ACM International Symposium on Microarchitecture, vol.4, pp.226-248, 1975.