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Found 12 papers, 7 papers with code
Robust multimodal models have outlier features and encode more concepts
In this work, we bridge this gap by probing the representation spaces of 12 robust multimodal models with various backbones (ResNets and ViTs) and pretraining sets (OpenAI, LAION-400M, LAION-2B, YFCC15M, CC12M and DataComp).
The Role of Entropy and Reconstruction in Multi-View Self-Supervised Learning
We consider a different lower bound on the MI consisting of an entropy and a reconstruction term (ER), and analyze the main MVSSL families through its lens.
DUET: 2D Structured and Approximately Equivariant Representations
We propose 2D strUctured and EquivarianT representations (coined DUET), which are 2d representations organized in a matrix structure, and equivariant with respect to transformations acting on the input data.
Adversarial Attacks on Graph Classifiers via Bayesian Optimisation
While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.
Challenges of Adversarial Image Augmentations
Image augmentations applied during training are crucial for the generalization performance of image classifiers.
Adversarial Attacks on Graph Classification via Bayesian Optimisation
While the majority of the literature focuses on such vulnerability in node-level classification tasks, little effort has been dedicated to analysing adversarial attacks on graph-level classification, an important problem with numerous real-life applications such as biochemistry and social network analysis.
Attacking Graph Classification via Bayesian Optimisation
Graph neural networks have been shown to be vulnerable to adversarial attacks.
On Invariance Penalties for Risk Minimization
We discuss the role of its eigenvalues in the relationship between the risk and the invariance penalty, and demonstrate that it is ill-conditioned for said counterexamples.
Adversarial Robustness Guarantees for Gaussian Processes
Gaussian processes (GPs) enable principled computation of model uncertainty, making them attractive for safety-critical applications.
The Effect of Prior Lipschitz Continuity on the Adversarial Robustness of Bayesian Neural Networks
It is desirable, and often a necessity, for machine learning models to be robust against adversarial attacks.
BayesOpt Adversarial Attack
Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.
Adversarial Robustness Guarantees for Classification with Gaussian Processes
We apply our method to investigate the robustness of GPC models on a 2D synthetic dataset, the SPAM dataset and a subset of the MNIST dataset, providing comparisons of different GPC training techniques, and show how our method can be used for interpretability analysis.
