Guardado en:
| Autores principales: | Yu, Yunrui, Su, Hang, Zhu, Jun |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2603.23860 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function
por: Yu, Yunrui, et al.
Publicado: (2025)
por: Yu, Yunrui, et al.
Publicado: (2025)
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
por: Yu, Yunrui, et al.
Publicado: (2025)
por: Yu, Yunrui, et al.
Publicado: (2025)
Ignition Phase : Standard Training for Fast Adversarial Robustness
por: Yu-Hang, Wang, et al.
Publicado: (2025)
por: Yu-Hang, Wang, et al.
Publicado: (2025)
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
por: Cao, Yuan-dong, et al.
Publicado: (2026)
por: Cao, Yuan-dong, et al.
Publicado: (2026)
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
por: Khachaturov, David, et al.
Publicado: (2024)
por: Khachaturov, David, et al.
Publicado: (2024)
Deriving the Scaled-Dot-Function via Maximum Likelihood Estimation and Maximum Entropy Approach
por: Ma, Jiyong
Publicado: (2025)
por: Ma, Jiyong
Publicado: (2025)
Robust Deep Reinforcement Learning against Adversarial Behavior Manipulation
por: Yamabe, Shojiro, et al.
Publicado: (2024)
por: Yamabe, Shojiro, et al.
Publicado: (2024)
Why Do Neural Networks Forget: A Study of Collapse in Continual Learning
por: Zhu, Yunqin, et al.
Publicado: (2026)
por: Zhu, Yunqin, et al.
Publicado: (2026)
Maintaining Adversarial Robustness in Continuous Learning
por: Ru, Xiaolei, et al.
Publicado: (2024)
por: Ru, Xiaolei, et al.
Publicado: (2024)
Total Variation Rates for Riemannian Flow Matching
por: Guan, Yunrui, et al.
Publicado: (2026)
por: Guan, Yunrui, et al.
Publicado: (2026)
Adversarial Robustness Overestimation and Instability in TRADES
por: Li, Jonathan Weiping, et al.
Publicado: (2024)
por: Li, Jonathan Weiping, et al.
Publicado: (2024)
Why Uncertainty Calibration Matters for Reliable Perturbation-based Explanations
por: Decker, Thomas, et al.
Publicado: (2025)
por: Decker, Thomas, et al.
Publicado: (2025)
Exploratory Diffusion Model for Unsupervised Reinforcement Learning
por: Ying, Chengyang, et al.
Publicado: (2025)
por: Ying, Chengyang, et al.
Publicado: (2025)
Towards Safe Reinforcement Learning via Constraining Conditional Value-at-Risk
por: Ying, Chengyang, et al.
Publicado: (2022)
por: Ying, Chengyang, et al.
Publicado: (2022)
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better
por: Zhou, MingWei, et al.
Publicado: (2025)
por: Zhou, MingWei, et al.
Publicado: (2025)
Adversarial Preference Learning for Robust LLM Alignment
por: Wang, Yuanfu, et al.
Publicado: (2025)
por: Wang, Yuanfu, et al.
Publicado: (2025)
Adversarially Robust Decision Transformer
por: Tang, Xiaohang, et al.
Publicado: (2024)
por: Tang, Xiaohang, et al.
Publicado: (2024)
Reinforced Compressive Neural Architecture Search for Versatile Adversarial Robustness
por: Wang, Dingrong, et al.
Publicado: (2024)
por: Wang, Dingrong, et al.
Publicado: (2024)
Why Adam Can Beat SGD: Second-Moment Normalization Yields Sharper Tails
por: Jin, Ruinan, et al.
Publicado: (2026)
por: Jin, Ruinan, et al.
Publicado: (2026)
On the Importance of Backbone to the Adversarial Robustness of Object Detectors
por: Li, Xiao, et al.
Publicado: (2023)
por: Li, Xiao, et al.
Publicado: (2023)
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
por: Rossolini, Giulio
Publicado: (2026)
por: Rossolini, Giulio
Publicado: (2026)
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
por: Pal, Ambar, et al.
Publicado: (2023)
por: Pal, Ambar, et al.
Publicado: (2023)
Adversarial Diffusion for Robust Reinforcement Learning
por: Foffano, Daniele, et al.
Publicado: (2025)
por: Foffano, Daniele, et al.
Publicado: (2025)
Algorithms for Adversarially Robust Deep Learning
por: Robey, Alexander
Publicado: (2025)
por: Robey, Alexander
Publicado: (2025)
Robust Decision Aggregation with Adversarial Experts
por: Guo, Yongkang, et al.
Publicado: (2024)
por: Guo, Yongkang, et al.
Publicado: (2024)
The Pragmatic Frames of Spurious Correlations in Machine Learning: Interpreting How and Why They Matter
por: Bell, Samuel J., et al.
Publicado: (2024)
por: Bell, Samuel J., et al.
Publicado: (2024)
Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness
por: Wang, Longwei, et al.
Publicado: (2025)
por: Wang, Longwei, et al.
Publicado: (2025)
A Comprehensive Survey of Continual Learning: Theory, Method and Application
por: Wang, Liyuan, et al.
Publicado: (2023)
por: Wang, Liyuan, et al.
Publicado: (2023)
GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models
por: Li, Zaitang, et al.
Publicado: (2023)
por: Li, Zaitang, et al.
Publicado: (2023)
Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems
por: He, Jun, et al.
Publicado: (2026)
por: He, Jun, et al.
Publicado: (2026)
Subgroups Matter for Robust Bias Mitigation
por: Alloula, Anissa, et al.
Publicado: (2025)
por: Alloula, Anissa, et al.
Publicado: (2025)
Does Order Matter : Connecting The Law of Robustness to Robust Generalization
por: Mandal, Himadri, et al.
Publicado: (2026)
por: Mandal, Himadri, et al.
Publicado: (2026)
CEAR: Certified Ensemble Adversarial Robustness in DNNs
por: Sadig, Daniel, et al.
Publicado: (2026)
por: Sadig, Daniel, et al.
Publicado: (2026)
ROKA: Robust Knowledge Unlearning against Adversaries
por: Shin, Jinmyeong, et al.
Publicado: (2026)
por: Shin, Jinmyeong, et al.
Publicado: (2026)
Adversarial Robustness of VAEs across Intersectional Subgroups
por: Ramanaik, Chethan Krishnamurthy, et al.
Publicado: (2024)
por: Ramanaik, Chethan Krishnamurthy, et al.
Publicado: (2024)
Towards Adversarially Robust Deep Metric Learning
por: Ke, Xiaopeng
Publicado: (2025)
por: Ke, Xiaopeng
Publicado: (2025)
Are Robust LLM Fingerprints Adversarially Robust?
por: Nasery, Anshul, et al.
Publicado: (2025)
por: Nasery, Anshul, et al.
Publicado: (2025)
TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
por: YuHang, Wang, et al.
Publicado: (2025)
por: YuHang, Wang, et al.
Publicado: (2025)
Infinite Width Models That Work: Why Feature Learning Doesn't Matter as Much as You Think
por: Sernau, Luke
Publicado: (2024)
por: Sernau, Luke
Publicado: (2024)
Data-Driven Lipschitz Continuity: A Cost-Effective Approach to Improve Adversarial Robustness
por: Chen, Erh-Chung, et al.
Publicado: (2024)
por: Chen, Erh-Chung, et al.
Publicado: (2024)
Ejemplares similares
-
RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function
por: Yu, Yunrui, et al.
Publicado: (2025) -
Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss
por: Yu, Yunrui, et al.
Publicado: (2025) -
Ignition Phase : Standard Training for Fast Adversarial Robustness
por: Yu-Hang, Wang, et al.
Publicado: (2025) -
When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
por: Cao, Yuan-dong, et al.
Publicado: (2026) -
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
por: Khachaturov, David, et al.
Publicado: (2024)