Saved in:
| Main Authors: | Bell, Brian, Geyer, Michael, Glickenstein, David, Hamm, Keaton, Scheidegger, Carlos, Fernandez, Amanda, Moore, Juston |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.08069 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics
by: Brian Bell, et al.
Published: (2025)
by: Brian Bell, et al.
Published: (2025)
Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
by: Rana, Muhammad, et al.
Published: (2025)
by: Rana, Muhammad, et al.
Published: (2025)
On Wasserstein distances for affine transformations of random vectors
by: Hamm, Keaton, et al.
Published: (2023)
by: Hamm, Keaton, et al.
Published: (2023)
Wasserstein approximation schemes based on Voronoi partitions
by: Hamm, Keaton, et al.
Published: (2023)
by: Hamm, Keaton, et al.
Published: (2023)
Efficient Analysis of the Distilled Neural Tangent Kernel
by: Mahowald, Jamie, et al.
Published: (2026)
by: Mahowald, Jamie, et al.
Published: (2026)
LCANets++: Robust Audio Classification using Multi-layer Neural Networks with Lateral Competition
by: Dibbo, Sayanton V., et al.
Published: (2023)
by: Dibbo, Sayanton V., et al.
Published: (2023)
A Geometric Framework for Adversarial Vulnerability in Machine Learning
by: Bell, Brian
Published: (2024)
by: Bell, Brian
Published: (2024)
Recovering Wasserstein Distance Matrices from Few Measurements
by: Rana, Muhammad, et al.
Published: (2025)
by: Rana, Muhammad, et al.
Published: (2025)
How Robust Are Energy-Based Models Trained With Equilibrium Propagation?
by: Mansingh, Siddharth, et al.
Published: (2024)
by: Mansingh, Siddharth, et al.
Published: (2024)
LOTFormer: Doubly-Stochastic Linear Attention via Low-Rank Optimal Transport
by: Shahbazi, Ashkan, et al.
Published: (2025)
by: Shahbazi, Ashkan, et al.
Published: (2025)
Manifold learning in Wasserstein space
by: Hamm, Keaton, et al.
Published: (2023)
by: Hamm, Keaton, et al.
Published: (2023)
A New Type of Adversarial Examples
by: Nie, Xingyang, et al.
Published: (2025)
by: Nie, Xingyang, et al.
Published: (2025)
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
by: Dibbo, Sayanton V., et al.
Published: (2024)
by: Dibbo, Sayanton V., et al.
Published: (2024)
Towards Interpretable Adversarial Examples via Sparse Adversarial Attack
by: Lin, Fudong, et al.
Published: (2025)
by: Lin, Fudong, et al.
Published: (2025)
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey
by: Karunanayake, Naveen, et al.
Published: (2024)
by: Karunanayake, Naveen, et al.
Published: (2024)
Evaluating Text Classification Robustness to Part-of-Speech Adversarial Examples
by: Samadi, Anahita, et al.
Published: (2024)
by: Samadi, Anahita, et al.
Published: (2024)
Transferable Adversarial Examples with Bayes Approach
by: Fan, Mingyuan, et al.
Published: (2022)
by: Fan, Mingyuan, et al.
Published: (2022)
1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem
by: Juston, Marius F. R., et al.
Published: (2025)
by: Juston, Marius F. R., et al.
Published: (2025)
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
by: Pal, Ambar, et al.
Published: (2023)
by: Pal, Ambar, et al.
Published: (2023)
Adversarial Examples Are Not Bugs, They Are Superposition
by: Gorton, Liv, et al.
Published: (2025)
by: Gorton, Liv, et al.
Published: (2025)
Detecting Adversarial Examples
by: Mumcu, Furkan, et al.
Published: (2024)
by: Mumcu, Furkan, et al.
Published: (2024)
Generating Less Certain Adversarial Examples Improves Robust Generalization
by: Zhang, Minxing, et al.
Published: (2023)
by: Zhang, Minxing, et al.
Published: (2023)
Transferability Ranking of Adversarial Examples
by: Levy, Mosh, et al.
Published: (2022)
by: Levy, Mosh, et al.
Published: (2022)
PCARNN-DCBF: Minimal-Intervention Geofence Enforcement for Ground Vehicles
by: Yu, Yinan, et al.
Published: (2025)
by: Yu, Yinan, et al.
Published: (2025)
LDLT L-Lipschitz Network Weight Parameterization Initialization
by: Juston, Marius F. R., et al.
Published: (2026)
by: Juston, Marius F. R., et al.
Published: (2026)
Laundering AI Authority with Adversarial Examples
by: Zhang, Jie, et al.
Published: (2026)
by: Zhang, Jie, et al.
Published: (2026)
Adversarial Examples Are Not Real Features
by: Li, Ang, et al.
Published: (2023)
by: Li, Ang, et al.
Published: (2023)
MemLoss: Enhancing Adversarial Training with Recycling Adversarial Examples
by: Mahdi, Soroush, et al.
Published: (2025)
by: Mahdi, Soroush, et al.
Published: (2025)
SoK: Analyzing Adversarial Examples: A Framework to Study Adversary Knowledge
by: Fenaux, Lucas, et al.
Published: (2024)
by: Fenaux, Lucas, et al.
Published: (2024)
Data-Dependent Stability Analysis of Adversarial Training
by: Wang, Yihan, et al.
Published: (2024)
by: Wang, Yihan, et al.
Published: (2024)
On Adversarial Examples for Text Classification by Perturbing Latent Representations
by: Sooksatra, Korn, et al.
Published: (2024)
by: Sooksatra, Korn, et al.
Published: (2024)
A Minimalist Example of Edge-of-Stability and Progressive Sharpening
by: Liu, Liming, et al.
Published: (2025)
by: Liu, Liming, et al.
Published: (2025)
Position: Towards Resilience Against Adversarial Examples
by: Dai, Sihui, et al.
Published: (2024)
by: Dai, Sihui, et al.
Published: (2024)
Alert-ME: An Explainability-Driven Defense Against Adversarial Examples in Transformer-Based Text Classification
by: Sabir, Bushra, et al.
Published: (2023)
by: Sabir, Bushra, et al.
Published: (2023)
LDLT $\mathcal{L}$-Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction
by: Juston, Marius F. R., et al.
Published: (2025)
by: Juston, Marius F. R., et al.
Published: (2025)
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
by: Lin, Runqi, et al.
Published: (2024)
by: Lin, Runqi, et al.
Published: (2024)
Laplacian Segmentation Networks Improve Epistemic Uncertainty Quantification
by: Zepf, Kilian, et al.
Published: (2023)
by: Zepf, Kilian, et al.
Published: (2023)
Rectifying Adversarial Examples Using Their Vulnerabilities
by: Morimoto, Fumiya, et al.
Published: (2026)
by: Morimoto, Fumiya, et al.
Published: (2026)
PEAS: A Strategy for Crafting Transferable Adversarial Examples
by: Avraham, Bar, et al.
Published: (2024)
by: Avraham, Bar, et al.
Published: (2024)
Analyzing the Impact of Adversarial Examples on Explainable Machine Learning
by: Devabhakthini, Prathyusha, et al.
Published: (2023)
by: Devabhakthini, Prathyusha, et al.
Published: (2023)
Similar Items
-
Persistent Classification: Understanding Adversarial Attacks by Studying Decision Boundary Dynamics
by: Brian Bell, et al.
Published: (2025) -
Neighbor Embeddings Using Unbalanced Optimal Transport Metrics
by: Rana, Muhammad, et al.
Published: (2025) -
On Wasserstein distances for affine transformations of random vectors
by: Hamm, Keaton, et al.
Published: (2023) -
Wasserstein approximation schemes based on Voronoi partitions
by: Hamm, Keaton, et al.
Published: (2023) -
Efficient Analysis of the Distilled Neural Tangent Kernel
by: Mahowald, Jamie, et al.
Published: (2026)