Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2017 | international conference on machine learning | Conference |
Volume | Citations | PageRank |
abs/1607.01097 | 23 | 0.88 |
References | Authors | |
30 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Corinna Cortes | 1 | 6574 | 1120.50 |
| Xavi Gonzalvo | 2 | 28 | 2.06 |
| Vitaly Kuznetsov | 3 | 68 | 9.33 |
| Mehryar Mohri | 4 | 4502 | 448.21 |
| Yang, Scott | 5 | 33 | 6.24 |