Poster
Generalization Bounds for Neural Networks via Approximate Description Length
Amit Daniely · Elad Granot
We investigate the sample complexity of networks with bounds on the magnitude of its weights.
In particular, we consider the class
\[
\cn = \left\{W_t\circ\rho\circ W_{t1}\circ\rho\ldots\circ \rho\circ W_{1} : W_1,\ldots,W_{t1}\in M_{d\times d}, W_t\in M_{1,d} \right\}
\]
where the spectral norm of each $W_i$ is bounded by $O(1)$, the Frobenius norm is bounded by $R$, and $\rho$ is the sigmoid function $\frac{e^x}{1 + e^x}$ or the smoothened ReLU function $ \ln\left(1 + e^x\right)$.
We show that for any depth $t$, if the inputs are in $[1,1]^d$, the sample complexity of $\cn$ is $\tilde O\left(\frac{dR^2}{\epsilon^2}\right)$. This bound is optimal up to logfactors, and substantially improves over the previous state of the art of $\tilde O\left(\frac{d^2R^2}{\epsilon^2}\right)$, that was established in a recent line of work.
We furthermore show that this bound remains valid if instead of considering the magnitude of the $W_i$'s, we consider the magnitude of $W_i  W_i^0$, where $W_i^0$ are some reference matrices, with spectral norm of $O(1)$. By taking the $W_i^0$ to be the matrices in the onset of the training process, we get sample complexity bounds that are sublinear in the number of parameters, in many {\em typical} regimes of parameters.
To establish our results we develop a new technique to analyze the sample complexity of families $\ch$ of predictors.
We start by defining a new notion of a randomized approximate description of functions $f:\cx\to\reals^d$. We then show that if there is a way to approximately describe functions in a class $\ch$ using $d$ bits, then $\frac{d}{\epsilon^2}$ examples suffices to guarantee uniform convergence. Namely, that the empirical loss of all the functions in the class is $\epsilon$close to the true loss. Finally, we develop a set of tools for calculating the approximate description length of classes of functions that can be presented as a composition of linear function classes and nonlinear functions.
Author Information
Amit Daniely (Hebrew University and Google Research)
Elad Granot (Hebrew University)
Related Events (a corresponding poster, oral, or spotlight)

2019 Spotlight: Generalization Bounds for Neural Networks via Approximate Description Length »
Wed Dec 11th 04:15  04:20 PM Room West Exhibition Hall C + B3
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