| Autor: |
Cortes, Corinna, Gonzalvo, Xavi, Kuznetsov, Vitaly, Mohri, Mehryar, Yang, Scott |
| Rok vydání: |
2016 |
| Předmět: |
|
| Druh dokumentu: |
Working Paper |
| Popis: |
We present new algorithms for adaptively learning artificial neural networks. Our algorithms (AdaNet) adaptively learn both the structure of the network and its weights. They are based on a solid theoretical analysis, including data-dependent generalization guarantees that we prove and discuss in detail. We report the results of large-scale experiments with one of our algorithms on several binary classification tasks extracted from the CIFAR-10 dataset. The results demonstrate that our algorithm can automatically learn network structures with very competitive performance accuracies when compared with those achieved for neural networks found by standard approaches. |
| Databáze: |
arXiv |
| Externí odkaz: |
|
