{"total":36,"rows":[{"id":"2303.03169","title":"A Unified Algebraic Perspective on Lipschitz Neural Networks","created":"2023-03-06","cited_by_count":60,"area":null},{"id":"2301.11526","title":"Direct Parameterization of Lipschitz-Bounded Deep Networks","created":"2023-01-27","cited_by_count":51,"area":null},{"id":"2302.10886","title":"Some Fundamental Aspects about Lipschitz Continuity of Neural Networks","created":"2023-02-21","cited_by_count":26,"area":null},{"id":"2310.00116","title":"Certified Robustness via Dynamic Margin Maximization and Improved Lipschitz Regularization","created":"2023-09-29","cited_by_count":21,"area":null},{"id":"2311.16833","title":"1-Lipschitz Layers Compared: Memory, Speed, and Certifiable Robustness","created":"2023-11-28","cited_by_count":16,"area":null},{"id":"2310.18274","title":"LipSim: A Provably Robust Perceptual Similarity Metric","created":"2023-10-27","cited_by_count":15,"area":null},{"id":"2402.01344","title":"Monotone, Bi-Lipschitz, and Polyak-Lojasiewicz Networks","created":"2024-02-02","cited_by_count":9,"area":null},{"id":"2407.09505","title":"1-Lipschitz Neural Distance Fields","created":"2024-06-14","cited_by_count":9,"area":null},{"id":"2502.20604","title":"Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness","created":"2025-02-28","cited_by_count":9,"area":null},{"id":"2501.07930","title":"An Adaptive Orthogonal Convolution Scheme for Efficient and Flexible CNN Architectures","created":"2025-01-14","cited_by_count":8,"area":null},{"id":"2401.14033","title":"Novel Quadratic Constraints for Extending LipSDP beyond Slope-Restricted Activations","created":"2024-01-25","cited_by_count":7,"area":null},{"id":"2402.02969","title":"Towards Understanding the Word Sensitivity of Attention Layers: A Study via Random Features","created":"2024-02-05","cited_by_count":5,"area":null},{"id":"2505.15174","title":"Enhancing Certified Robustness via Block Reflector Orthogonal Layers and Logit Annealing Loss","created":"2025-05-21","cited_by_count":5,"area":null},{"id":"2310.02513","title":"A Recipe for Improved Certifiable Robustness","created":"2023-10-04","cited_by_count":4,"area":null},{"id":"2402.00240","title":"Spectral Norm of Convolutional Layers with Circular and Zero Paddings","created":"2024-01-31","cited_by_count":4,"area":null},{"id":"2406.05119","title":"Compositional Curvature Bounds for Deep Neural Networks","created":"2024-06-07","cited_by_count":4,"area":null},{"id":"2309.06166","title":"Certified Robust Models with Slack Control and Large Lipschitz Constants","created":"2023-09-12","cited_by_count":3,"area":null},{"id":"2412.04245","title":"Intriguing Properties of Robust Classification","created":"2024-12-05","cited_by_count":3,"area":null},{"id":"2305.07618","title":"Uncertainty Estimation and Out-of-Distribution Detection for Deep Learning-Based Image Reconstruction using the Local Lipschitz","created":"2023-05-12","cited_by_count":2,"area":null},{"id":"2410.22258","title":"LipKernel: Lipschitz-Bounded Convolutional Neural Networks via Dissipative Layers","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2503.00240","title":"1-Lipschitz Network Initialization for Certifiably Robust Classification Applications: A Decay Problem","created":"2025-02-28","cited_by_count":2,"area":null},{"id":"2504.01250","title":"R2DN: Scalable Parameterization of Contracting and Lipschitz Recurrent Deep Networks","created":"2025-04-01","cited_by_count":2,"area":null},{"id":"2601.18513","title":"LipNeXt: Scaling up Lipschitz-based Certified Robustness to Billion-parameter Models","created":"2026-01-26","cited_by_count":2,"area":null},{"id":"2311.06103","title":"1-Lipschitz Neural Networks are more expressive with N-Activations","created":"2023-11-10","cited_by_count":1,"area":null},{"id":"2404.19112","title":"Hidden Synergy: $L_1$ Weight Normalization and 1-Path-Norm Regularization","created":"2024-04-29","cited_by_count":1,"area":null},{"id":"2410.05136","title":"LOTOS: Layer-wise Orthogonalization for Training Robust Ensembles","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2410.20432","title":"Integrating uncertainty quantification into randomized smoothing based robustness guarantees","created":"2024-10-27","cited_by_count":1,"area":null},{"id":"2512.04632","title":"Turbo-Muon: Almost-Orthogonal Pre-Conditioning for Fast Muon Updates","created":"2025-12-04","cited_by_count":1,"area":null},{"id":"2502.21279","title":"L-Lipschitz Gershgorin ResNet Network","created":"2025-02-28","cited_by_count":0,"area":null},{"id":"2503.09624","title":"APECS: Adaptive Personalized Control System Architecture","created":"2025-03-10","cited_by_count":0,"area":null},{"id":"2506.04951","title":"Robustness as Architecture: Designing IQA Models to Withstand Adversarial Perturbations","created":"2025-06-05","cited_by_count":0,"area":null},{"id":"2512.05915","title":"LDLT $\\mathcal{L}$-Lipschitz Network: Generalized Deep End-To-End Lipschitz Network Construction","created":"2025-12-05","cited_by_count":0,"area":null},{"id":"2601.08253","title":"LDLT L-Lipschitz Network Weight Parameterization Initialization","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2601.13776","title":"Orthogonium : A Unified, Efficient Library of Orthogonal and 1-Lipschitz Building Blocks","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2607.07745","title":"LiST: Lipschitz Scaling Training for Robust and Calibrated Neural Networks","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.30973","title":"LipSSM: Structurally Lipschitz-Bounded Cascaded State-Space Model via Metric Transfer between Consecutive SSM Layers","created":"2026-09-25","cited_by_count":0,"area":null}],"next_cursor":null}