2 papers
cs.LG2025
Growing Neural Networks: Dynamic Evolution through Gradient Descent
Anil Radhakrishnan, John F. Lindner, Scott T. Miller +2
In contrast to conventional artificial neural networks, which are structurally static, we present two approaches for evolving small networks into larger ones during training. The f…
nlin.AO2024
Evolution beats random chance: Performance-dependent network evolution for enhanced computational capacity
Manish Yadav, Sudeshna Sinha, Merten Stender
The quest to understand structure-function relationships in networks across scientific disciplines has intensified. However, the optimal network architecture remains elusive, parti…