49 citations · 93 across the 2 of their papers we have counts for
5 papers · 1 filter
Theory III: Dynamics and Generalization in Deep Networks
Andrzej Banburski, Qianli Liao, Brando Miranda +4
The key to generalization is controlling the complexity of the network. However, there is no obvious control of complexity -- such as an explicit regularization term -- in the trai…
A Surprising Linear Relationship Predicts Test Performance in Deep Networks
Qianli Liao, Brando Miranda, Andrzej Banburski +2
Given two networks with the same training loss on a dataset, when would they have drastically different test losses and errors? Better understanding of this question of generalizat…
Theory IIIb: Generalization in Deep Networks
Tomaso Poggio, Qianli Liao, Brando Miranda +3
A main puzzle of deep neural networks (DNNs) revolves around the apparent absence of "overfitting", defined in this paper as follows: the expected error does not get worse when inc…
Theory of Deep Learning III: explaining the non-overfitting puzzle
Tomaso Poggio, Kenji Kawaguchi, Qianli Liao +5
A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on ra…
Theory of Deep Learning IIb: Optimization Properties of SGD
Chiyuan Zhang, Qianli Liao, Alexander Rakhlin +3
In Theory IIb we characterize with a mix of theory and experiments the optimization of deep convolutional networks by Stochastic Gradient Descent. The main new result in this paper…