Exploring Adversarial Robustness of Deep Metric Learning

1 code implementation • 14 Feb 2021 • Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha

Deep Metric Learning (DML), a widely-used technique, involves learning a distance metric between pairs of samples.

Adversarial Robustness Metric Learning

Adversarial Deep Metric Learning

no code implementations • 1 Jan 2021 • Thomas Kobber Panum, Zi Wang, Pengyu Kan, Earlence Fernandes, Somesh Jha

To the best of our knowledge, we are the first to systematically analyze this dependence effect and propose a principled approach for robust training of deep metric learning networks that accounts for the nuances of metric losses.

Metric Learning

CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods

1 code implementation • ICML 2020 • Wei Zhang, Thomas Kobber Panum, Somesh Jha, Prasad Chalasani, David Page

We study the problem of learning Granger causality between event types from asynchronous, interdependent, multi-type event sequences.