### Abstract

We study the connection between the highly non-convex loss function of a simple model of the fully-connected feed-forward neural network and the Hamiltonian of the spherical spin-glass model under the assumptions of: i) variable independence, ii) redundancy in network parametrization, and iii) uniformity. These assumptions enable us to explain the complexity of the fully decoupled neural network through the prism of the results from random matrix theory. We show that for large-size decoupled networks the lowest critical values of the random loss function form a layered structure and they are located in a well-defined band lower-bounded by the global minimum. The number of local minima outside that band diminishes exponentially with the size of the network. We empirically verify that the mathematical model exhibits similar behavior as the computer simulations, despite the presence of high dependencies in real networks. We conjecture that both simulated annealing and SGD converge to the band of low critical points, and that all critical points found there are local minima of high quality measured by the test error. This emphasizes a major difference between large-and small-size networks where for the latter poor quality local minima have nonzero probability of being recovered. Finally, we prove that recovering the global minimum becomes harder as the network size increases and that it is in practice irrelevant as global minimum often leads to overfitting.

Original language | English (US) |
---|---|

Pages (from-to) | 192-204 |

Number of pages | 13 |

Journal | Journal of Machine Learning Research |

Volume | 38 |

State | Published - 2015 |

Event | 18th International Conference on Artificial Intelligence and Statistics, AISTATS 2015 - San Diego, United States Duration: May 9 2015 → May 12 2015 |

### ASJC Scopus subject areas

- Software
- Control and Systems Engineering
- Statistics and Probability
- Artificial Intelligence

## Fingerprint Dive into the research topics of 'The loss surfaces of multilayer networks'. Together they form a unique fingerprint.

## Cite this

*Journal of Machine Learning Research*,

*38*, 192-204.