{"total":31,"rows":[{"id":"2305.18887","title":"How Does Information Bottleneck Help Deep Learning?","created":"2023-05-30","cited_by_count":93,"area":null},{"id":"2110.12690","title":"A Dynamical System Perspective for Lipschitz Neural Networks","created":"2021-10-25","cited_by_count":60,"area":null},{"id":"2103.03046","title":"PointGuard: Provably Robust 3D Point Cloud Classification","created":"2021-03-04","cited_by_count":49,"area":null},{"id":"2110.04471","title":"Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning","created":"2021-10-09","cited_by_count":29,"area":null},{"id":"2307.15157","title":"R-LPIPS: An Adversarially Robust Perceptual Similarity Metric","created":"2023-07-27","cited_by_count":28,"area":null},{"id":"2110.02180","title":"Noisy Feature Mixup","created":"2021-10-05","cited_by_count":27,"area":null},{"id":"2102.06905","title":"Mixed Nash Equilibria in the Adversarial Examples Game","created":"2021-02-13","cited_by_count":22,"area":null},{"id":"2101.04829","title":"On the Effectiveness of Small Input Noise for Defending Against Query-based Black-Box Attacks","created":"2021-01-13","cited_by_count":17,"area":null},{"id":"2212.02531","title":"Enhancing Quantum Adversarial Robustness by Randomized Encodings","created":"2022-12-05","cited_by_count":17,"area":null},{"id":"2310.18274","title":"LipSim: A Provably Robust Perceptual Similarity Metric","created":"2023-10-27","cited_by_count":15,"area":null},{"id":"2402.13987","title":"A Simple and Yet Fairly Effective Defense for Graph Neural Networks","created":"2024-02-21","cited_by_count":12,"area":null},{"id":"2102.10875","title":"On the robustness of randomized classifiers to adversarial examples","created":"2021-02-22","cited_by_count":9,"area":null},{"id":"2101.02115","title":"Adversarial Robustness by Design through Analog Computing and Synthetic Gradients","created":"2021-01-06","cited_by_count":7,"area":null},{"id":"2310.08889","title":"PerturbScore: Connecting Discrete and Continuous Perturbations in NLP","created":"2023-10-13","cited_by_count":5,"area":null},{"id":"2106.06012","title":"Learning distinct features helps, provably","created":"2021-06-10","cited_by_count":4,"area":null},{"id":"2302.07221","title":"On the Role of Randomization in Adversarially Robust Classification","created":"2023-02-14","cited_by_count":4,"area":null},{"id":"2406.05532","title":"Exploring Adversarial Robustness of Deep State Space Models","created":"2024-06-08","cited_by_count":4,"area":null},{"id":"2210.14229","title":"Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network","created":"2022-10-25","cited_by_count":3,"area":null},{"id":"2404.08285","title":"A Survey of Neural Network Robustness Assessment in Image Recognition","created":"2024-04-12","cited_by_count":3,"area":null},{"id":"2108.04217","title":"ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients","created":"2021-07-06","cited_by_count":2,"area":null},{"id":"2201.00148","title":"Rethinking Feature Uncertainty in Stochastic Neural Networks for Adversarial Robustness","created":"2022-01-01","cited_by_count":2,"area":null},{"id":"2206.01715","title":"Towards Evading the Limits of Randomized Smoothing: A Theoretical Analysis","created":"2022-06-03","cited_by_count":2,"area":null},{"id":"2206.07826","title":"Metric-Fair Classifier Derandomization","created":"2022-06-15","cited_by_count":2,"area":null},{"id":"2204.10839","title":"How Sampling Impacts the Robustness of Stochastic Neural Networks","created":"2022-04-22","cited_by_count":1,"area":null},{"id":"2208.06228","title":"Unifying Gradients to Improve Real-world Robustness for Deep Networks","created":"2022-08-12","cited_by_count":1,"area":null},{"id":"2105.14710","title":"Robustifying $\\ell_\\infty$ Adversarial Training to the Union of Perturbation Models","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2112.15111","title":"Improving the Behaviour of Vision Transformers with Token-consistent Stochastic Layers","created":"2021-12-30","cited_by_count":0,"area":null},{"id":"2306.16738","title":"Towards Optimal Randomized Strategies in Adversarial Example Game","created":"2023-06-29","cited_by_count":0,"area":null},{"id":"2402.04325","title":"Enhance DNN Adversarial Robustness and Efficiency via Injecting Noise to Non-Essential Neurons","created":"2024-02-06","cited_by_count":0,"area":null},{"id":"2503.14299","title":"Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory","created":"2025-03-18","cited_by_count":0,"area":null},{"id":"2511.21799","title":"The Double-Edged Nature of the Rashomon Set for Trustworthy Machine Learning","created":"2025-11-26","cited_by_count":0,"area":null}],"next_cursor":null}