Search Results for author: Devin Willmott

Found 6 papers, 5 papers with code

You Only Query Once: Effective Black Box Adversarial Attacks with Minimal Repeated Queries

no code implementations29 Jan 2021 Devin Willmott, Anit Kumar Sahu, Fatemeh Sheikholeslami, Filipe Condessa, Zico Kolter

In this work, we instead show that it is possible to craft (universal) adversarial perturbations in the black-box setting by querying a sequence of different images only once.

Multiplicative Filter Networks

3 code implementations ICLR 2021 Rizal Fathony, Anit Kumar Sahu, Devin Willmott, J Zico Kolter

Although deep networks are typically used to approximate functions over high dimensional inputs, recent work has increased interest in neural networks as function approximators for low-dimensional-but-complex functions, such as representing images as a function of pixel coordinates, solving differential equations, or representing signed distance fields or neural radiance fields.

Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes

1 code implementation13 Jul 2020 Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input.

Black-box Adversarial Attacks with Bayesian Optimization

1 code implementation30 Sep 2019 Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs.

Orthogonal Recurrent Neural Networks with Scaled Cayley Transform

2 code implementations ICML 2018 Kyle Helfrich, Devin Willmott, Qiang Ye

Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.

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