5 citations · 5 across the 2 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
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…
cs.LG2020
Forecasting Hamiltonian dynamics without canonical coordinates
Anshul Choudhary, John F. Lindner, Elliott G. Holliday +3
Conventional neural networks are universal function approximators, but because they are unaware of underlying symmetries or physical laws, they may need impractically many training…