Abstract
One of the main difficulties in analyzing neural networks is the non-convexity of the loss function which may have many bad local minima. In this paper, we study the landscape of neural networks for binary classification tasks. Under mild assumptions, we prove that after adding one special neuron with a skip connection to the output, or one special neuron per layer, every local minimum is a global minimum.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4350-4360 |
| Number of pages | 11 |
| Journal | Advances in Neural Information Processing Systems |
| Volume | 2018-December |
| State | Published - 2018 |
| Event | 32nd Conference on Neural Information Processing Systems, NeurIPS 2018 - Montreal, Canada Duration: Dec 2 2018 → Dec 8 2018 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing