2 papers
cs.LG2026
Neural delay differential equations: learning non-Markovian closures for partially known dynamical systems
Thibault Monsel, Onofrio Semeraro, Lionel Mathelin +1
Recent advances in learning dynamical systems from data have shown significant promise. However, many existing methods assume access to the full state of the system -- an assumptio…
cs.LG2025
Growth strategies for arbitrary DAG neural architectures
Stella Douka, Manon Verbockhaven, Théo Rudkiewicz +4
Deep learning has shown impressive results obtained at the cost of training huge neural networks. However, the larger the architecture, the higher the computational, financial, and…