arXiv subjects

Avi Schwarzschild

Publications and source records attributed to Avi Schwarzschild.

39 records · Page 3Linked to original sources

arXiv2020

Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory

We empirically evaluate common assumptions about neural networks that are widely held by practitioners and theorists alike. In this work, we: (1) prove the widespread existence of suboptimal local minima in the loss landscape of neural networks, and we use our theory to find examples; (2) show that small-norm parameters are not optimal for generalization; (3) demonstrate that ResNets do not conform to wide-network theories, such as the neural tangent kernel, and that the interaction between skip connections and batch normalization plays a role; (4) find that rank does not correlate with generalization or robustness in a practical setting.

cs.LG↗

arXiv2020

Headless Horseman: Adversarial Attacks on Transfer Learning Models

Transfer learning facilitates the training of task-specific classifiers using pre-trained models as feature extractors. We present a family of transferable adversarial attacks against such classifiers, generated without access to the classification head; we call these \emph{headless attacks}. We first demonstrate successful transfer attacks against a victim network using \textit{only} its feature extractor. This motivates the introduction of a label-blind adversarial attack. This transfer attack method does not require any information about the class-label space of the victim. Our attack lowers the accuracy of a ResNet18 trained on CIFAR10 by over 40\%.

cs.LG↗

arXiv2018

An Implementation of Adaptive Mesh Refinement for Shallow Water Equations

An implementation of adaptive mesh refinement algorithms is presented for use with multilayer shallow water equations. Currently, adaptive mesh refinement is implemented with a single layer shallow water model in the GeoClaw framework. This implementation, also in the GeoClaw framework, is for multilayer models, which have been implemented in GeoClaw previously. Until now, however, these models were too computationally expensive to run on large domains while resolving detail in coastal regions.

math.NA↗