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no code implementations • 29 Sep 2021 • Dhrupad Bhardwaj, Julia Kempe, Artem M Vysogorets, Angela Teng, Evaristus Ezekwem

Starting from existing work on network masking (Wortsman et al., 2020), we show that a simple to learn linear combination of a small number of task-specific masks (”impressions”) ona randomly initialized backbone network is sufficient to both retain accuracy on previously learned tasks, as well as achieve high accuracy on new tasks.

no code implementations • 29 Sep 2021 • Nikolaos Tsilivis, Julia Kempe

In particular, in the regime where the Neural Tangent Kernel theory holds, we derive a simple, but powerful strategy for attacking models, which in contrast to prior work, does not require any access to the model under attack, or any trained replica of it for that matter.

1 code implementation • 5 Jul 2021 • Artem Vysogorets, Julia Kempe

Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes.

1 code implementation • 13 Mar 2003 • Julia Kempe

This article aims to provide an introductory survey on quantum random walks.

Quantum Physics Data Structures and Algorithms

no code implementations • 18 Dec 2000 • Dorit Aharonov, Andris Ambainis, Julia Kempe, Umesh Vazirani

We set the ground for a theory of quantum walks on graphs- the generalization of random walks on finite graphs to the quantum world.

Quantum Physics

no code implementations • 15 Aug 2000 • Dave Bacon, Andrew M. Childs, Isaac L. Chuang, Julia Kempe, Debbie W. Leung, Xinlan Zhou

Although the conditions for performing arbitrary unitary operations to simulate the dynamics of a closed quantum system are well understood, the same is not true of the more general class of quantum operations (also known as superoperators) corresponding to the dynamics of open quantum systems.

Quantum Physics

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