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Found 11 papers, 2 papers with code
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off
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.
Tight Certified Robustness via Min-Max Representations of ReLU Neural Networks
The reliable deployment of neural networks in control systems requires rigorous robustness guarantees.
Projected Randomized Smoothing for Certified Adversarial Robustness
Randomized smoothing is the current state-of-the-art method for producing provably robust classifiers.
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
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)
An Overview and Prospective Outlook on Robust Training and Certification of Machine Learning Models
In this discussion paper, we survey recent research surrounding robustness of machine learning models.
Node-Variant Graph Filters in Graph Neural Networks
We show that, by replacing nonlinear activation functions by NVGFs, frequency creation mechanisms can be designed or learned.
Towards Optimal Branching of Linear and Semidefinite Relaxations for Neural Network Robustness Certification
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.
Certifying Neural Network Robustness to Random Input Noise from Samples
Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings.
Data-Driven Certification of Neural Networks with Random Input Noise
Methods to certify the robustness of neural networks in the presence of input uncertainty are vital in safety-critical settings.
Tightened Convex Relaxations for Neural Network Robustness Certification
In this paper, we consider the problem of certifying the robustness of neural networks to perturbed and adversarial input data.
Global Optimality Guarantees for Nonconvex Unsupervised Video Segmentation
In this paper, we consider the problem of unsupervised video object segmentation via background subtraction.
