deep learning – cmc.deusto.eus

Neural ODE Control for Classification, Approximation and Transport

data classification, deep learning, Neural ODEs, Optimal Transport, simultaneous control, transport equations, universal approximation, Wasserstein distance

Ruiz-Balet D., Zuazua E. Neural ODE Control for Classification, Approximation and Transport (2023), SIAM Review, Vol. 65, No. 3, pp. 735-773, https://doi.org/10.1137/21M1411433 Abstract. We analyze Neural Ordinary Differential Equations (NODEs)…

Sparse approximation in learning via neural ODEs

deep learning, Neural ODEs, Nonlinear systems, optimal control, Sparsity, Supervised Learning

Esteve C., Geshkovski B. Sparse approximation in learning via neural ODEs (2021) Abstract. We consider the continuous-time, neural ordinary differential equation (neural ODE) perspective of deep supervised learning, and study the…

Large-time asymptotics in deep learning

deep learning, Neural ODEs, optimal control, Residual Neural Networks, Supervised Learning, turnpike property

Esteve C., Geshkovski B., Pighin D., Zuazua E. Large-time asymptotics in deep learning (2021). hal-02912516 Abstract. It is by now well-known that practical deep supervised learning may roughly be cast as…

HKSIAM Seminar by E. Zuazua: Turnpike control and deep learning

deep learning, DL, Turnpike

Worldwide. This wednesday November 25th. our Head Enrique Zuazua will be talking about "Turnpike control and deep learning" in the Applied Mathematics Seminars for HKSIAM and Hong Kong Universities organized…

Large-time asymptotics in Deep Learning by Borjan Geshkovski

deep learning

Last Friday October 23rd our team member Borjan Geshkovski PhD student at DyCon ERC Project from UAM, gave a talk at "Seminario de estadística" organized by the UAM - Universidad…

Turnpike Control and Deep Learning by Enrique Zuazua at IITK

deep learning, Neural ODEs, optimal control, Residual Neural Networks, Supervised Learning, turnpike property

Worldwide. 08.09.2020. Yesterday, our Head Enrique Zuazua gave a talk about "Turnpike Control and Deep Learning" hosted by IITK - Indian Institute of Technology, Kampur in collaboration with IISER-Kolkata, IISER-Pune,…

Turnpike Control and Deep Learning at IITK by E. Zuazua

Next tuesday September 8th, our Head Enrique Zuazua will present "Turnpike Control and Deep Learning" on the IITK research seminars organized by IITK- Indian Institute of Technology, Kanpur in collaboration with…

Turnpike Control and Deep Learning – Fields Institute 2nd Simposium on Machine Learning and Dynamical Systems

deep learning, Neural ODEs, optimal control, Residual Neural Networks, Supervised Learning, turnpike property

Worldwide. 01.09.2020. The Fields Institute for Research in Mathematical Sciences at the University of Toronto, Canada organized the Second Simposium on Machine Learning and Dynamical Systems on September 21st to…

Neural ODE Control for Classification, Approximation and Transport

Sparse approximation in learning via neural ODEs

Large-time asymptotics in deep learning

HKSIAM Seminar by E. Zuazua: Turnpike control and deep learning

Large-time asymptotics in Deep Learning by Borjan Geshkovski

Turnpike Control and Deep Learning by Enrique Zuazua at IITK

Turnpike Control and Deep Learning at IITK by E. Zuazua

Turnpike Control and Deep Learning – Fields Institute 2nd Simposium on Machine Learning and Dynamical Systems

An inverse problem for Moore-Gibson-Thompson equation arising in high intensity ultrasound

Averaged turnpike property for differential equations with random constant coefficients. Mathematical Control and Related Fields

Boundary Controllability for the 1D Moore-Gibson-Thompson equation

Controllability properties from the exterior under positivity constraints for a 1-D fractional heat equation

Discrete Carleman estimates and application to controllability for a fully-discrete parabolic operator with dynamic boundary conditions