|Title||Theory of Deep Learning III: Generalization Properties of SGD|
|Publication Type||CBMM Memos|
|Year of Publication||2017|
|Authors||Zhang, C, Liao, Q, Rakhlin, A, Sridharan, K, Miranda, B, Golowich, N, Poggio, T|
In Theory III we characterize with a mix of theory and experiments the generalization properties of Stochastic Gradient Descent in overparametrized deep convolutional networks. We show that Stochastic Gradient Descent (SGD) selects with high probability solutions that 1) have zero (or small) empirical error, 2) are degenerate as shown in Theory II and 3) have maximum generalization.
CBMM Memo No:
- CBMM Funded