no code implementations • 27 May 2024 • Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Our experiments provide strong evidence that respecting the underlying algebraic structure of the input space is key for learning accurate and self-consistent operations.
no code implementations • 26 Nov 2023 • Yatong Bai, Brendon G. Anderson, Somayeh Sojoudi
However, standard learning models often suffer from an accuracy-robustness trade-off, which is a limitation that must be overcome in the control of safety-critical systems that require both high performance and rigorous robustness guarantees.
no code implementations • 7 Oct 2023 • Brendon G. Anderson, Samuel Pfrommer, Somayeh Sojoudi
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees.
1 code implementation • 25 Sep 2023 • Samuel Pfrommer, Brendon G. Anderson, Somayeh Sojoudi
Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers.
1 code implementation • 29 Jan 2023 • Yatong Bai, Brendon G. Anderson, Aerin Kim, Somayeh Sojoudi
While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to their unacceptably severe clean accuracy penalties.
Ranked #1 on Adversarial Robustness on CIFAR-100 (using extra training data)
no code implementations • 15 Aug 2022 • Brendon G. Anderson, Tanmay Gautam, Somayeh Sojoudi
In this discussion paper, we survey recent research surrounding robustness of machine learning models.
no code implementations • 31 May 2021 • Fernando Gama, Brendon G. Anderson, Somayeh Sojoudi
We show that, by replacing nonlinear activation functions by NVGFs, frequency creation mechanisms can be designed or learned.
no code implementations • 22 Jan 2021 • Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi
We extend the analysis to the SDP, where the feasible set geometry is exploited to design a branching scheme that minimizes the worst-case SDP relaxation error.
no code implementations • 15 Oct 2020 • Brendon G. Anderson, Somayeh Sojoudi
Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings.
no code implementations • 2 Oct 2020 • Brendon G. Anderson, Somayeh Sojoudi
Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings.
no code implementations • 1 Apr 2020 • Brendon G. Anderson, Ziye Ma, Jingqi Li, Somayeh Sojoudi
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data.
no code implementations • 9 Jul 2019 • Brendon G. Anderson, Somayeh Sojoudi
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction.