Abstract
We present experiments demonstrating that some other form of capacity
control, different from network size, plays a central role in learning
multilayer feed-forward networks. We argue, partially through analogy to matrix
factorization, that this is an inductive bias that can help shed light on deep
learning.
Users
Please
log in to take part in the discussion (add own reviews or comments).