Jonathan S Rosenfeld

2 accepted papers

2021

On the Predictability of Pruning Across Scales

Jonathan S Rosenfeld, Jonathan Frankle, Michael Carbin, Nir Shavit

ICML 2021spotlight

We show that the error of iteratively magnitude-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We functionally approximate the error of the pruned networks, showing it is predictable in terms of an invariant tying width, de…

Cited by 42 Source ↗PDF ↗Scholar ↗

2020

A Constructive Prediction of the Generalization Error Across Scales

Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit

ICLR 2020poster

The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form whi…

Cited by 223 Source ↗Scholar ↗