Martin Hofmann

When does a network's training history predict its future learning better than its current state? Evidence from a response probe and a forecasting screen

Networks that behave alike now can still learn differently when training continues. Work on loss of plasticity and critical periods shows that the path to a state shapes what follows; it does not show whether the path carries information that a measurement of the state itself misses. We ask when the training history of...