61 citations · 61 across the 3 of their papers we have counts for
6 papers
Universe Points Representation Learning for Partial Multi-Graph Matching
Zhakshylyk Nurlanov, Frank R. Schmidt, Florian Bernard
Many challenges from natural world can be formulated as a graph matching problem. Previous deep learning-based methods mainly consider a full two-graph matching setting. In this wo…
Neural Network Virtual Sensors for Fuel Injection Quantities with Provable Performance Specifications
Eric Wong, Tim Schneider, Joerg Schmitt +2
Recent work has shown that it is possible to learn neural networks with provable guarantees on the output of the model when subject to input perturbations, however these works have…
Adversarial camera stickers: A physical camera-based attack on deep learning systems
Juncheng Li, Frank R. Schmidt, J. Zico Kolter
Recent work has documented the susceptibility of deep learning systems to adversarial examples, but most such attacks directly manipulate the digital input to a classifier. Althoug…
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Eric Wong, Frank R. Schmidt, J. Zico Kolter
A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works ha…
Scaling provable adversarial defenses
Eric Wong, Frank R. Schmidt, Jan Hendrik Metzen +1
Recent work has developed methods for learning deep network classifiers that are provably robust to norm-bounded adversarial perturbation; however, these methods are currently only…
Compression for Smooth Shape Analysis
V. Estellers, F. R. Schmidt, D. Cremers
Most 3D shape analysis methods use triangular meshes to discretize both the shape and functions on it as piecewise linear functions. With this representation, shape analysis requir…