Deep networks are usually trained and tested in a regime in which the training classification error is not a good predictor of the test error. Thus the consensus has been that generalization, defined as convergence of the empirical to the expected error, does not hold for deep networks. Here we show that, when normalized appropriately after training, deep networks trained on exponential type losses show a good linear dependence of test loss on training loss. The observation, motivated by a previous theoretical analysis of overparametrization and overfitting, not only demonstrates the validity of classical generalization bounds for deep learning but suggests that they are tight. In addition, we also show that the bound of the classification error by the normalized cross entropy loss is empirically rather tight on the data sets we studied.

%8 07/2018 %1 %2http://hdl.handle.net/1721.1/116911

%0 Generic %D 2018 %T Theory III: Dynamics and Generalization in Deep Networks %A Andrzej Banburski %A Qianli Liao %A Brando Miranda %A Tomaso Poggio %A Lorenzo Rosasco %A Jack Hidary %A Fernanda De La Torre %XThe 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 training of deep networks for classification. We

will show that a classical form of norm control -- but

kind of hidden -- is responsible for good expected

performance by

deep networks trained with gradient descent techniques on

exponential-type losses. In particular, gradient descent

induces a dynamics of the normalized weights which

converge for $t \to \infty$ to an equilibrium which

corresponds to a minimum norm (or maximum margin)

solution. For sufficiently large but finite $\rho$ -- and

thus fnite $t$ -- the dynamics converges to one of several

hyperbolic minima corresponding to a regularized,

constrained minimizer -- the network with normalized

weights-- which is stable and generalizes. At the limit,

generalizaton is lost but the minimum norm property of the

solution provides, we conjecture, good expected

performance. Our approach extends some of the results of

Srebro from linear networks to deep networks and provides

a new perspective on the implicit bias of gradient

descent. The elusive complexity control we describe is

responsible, at least in part, for the puzzling empirical

finding of good predictive performance by deep networks, despite

overparametrization.

%8 06/2018 %2

http://hdl.handle.net/1721.1/116692

%0 Generic %D 2017 %T Theory of Deep Learning III: explaining the non-overfitting puzzle %A Tomaso Poggio %A Keji Kawaguchi %A Qianli Liao %A Brando Miranda %A Lorenzo Rosasco %A Xavier Boix %A Jack Hidary %A Hrushikesh Mhaskar %X**THIS MEMO IS REPLACED BY CBMM MEMO 90**

A main puzzle of deep networks revolves around the absence of overfitting despite overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamical systems associated with gradient descent minimization of nonlinear networks behave near zero stable minima of the empirical error as gradient system in a quadratic potential with degenerate Hessian. The proposition is supported by theoretical and numerical results, under the assumption of stable minima of the gradient.

Our proposition provides the extension to deep networks of key properties of gradient descent methods for linear networks, that as, suggested in (1), can be the key to understand generalization. Gradient descent enforces a form of implicit regular- ization controlled by the number of iterations, and asymptotically converging to the minimum norm solution. This implies that there is usually an optimum early stopping that avoids overfitting of the loss (this is relevant mainly for regression). For classification, the asymptotic convergence to the minimum norm solution implies convergence to the maximum margin solution which guarantees good classification error for “low noise” datasets.

The implied robustness to overparametrization has suggestive implications for the robustness of deep hierarchically local networks to variations of the architecture with respect to the curse of dimensionality.

%8 12/2017 %1 %2