{"total":36,"rows":[{"id":"2306.02010","title":"Memorization Capacity of Multi-Head Attention in Transformers","created":"2023-06-03","cited_by_count":40,"area":null},{"id":"2406.03334","title":"Reparameterization invariance in approximate Bayesian inference","created":"2024-06-05","cited_by_count":17,"area":null},{"id":"2410.04887","title":"Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse","created":"2024-10-07","cited_by_count":16,"area":null},{"id":"2302.01629","title":"Beyond the Universal Law of Robustness: Sharper Laws for Random Features and Neural Tangent Kernels","created":"2023-02-03","cited_by_count":13,"area":null},{"id":"2311.01575","title":"On the Convergence of Encoder-only Shallow Transformers","created":"2023-11-02","cited_by_count":13,"area":null},{"id":"2404.12928","title":"The Positivity of the Neural Tangent Kernel","created":"2024-04-19","cited_by_count":13,"area":null},{"id":"2405.14630","title":"Bounds for the smallest eigenvalue of the NTK for arbitrary spherical data of arbitrary dimension","created":"2024-05-23","cited_by_count":13,"area":null},{"id":"2305.12100","title":"How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features","created":"2023-05-20","cited_by_count":11,"area":null},{"id":"2305.19377","title":"Benign Overfitting in Deep Neural Networks under Lazy Training","created":"2023-05-30","cited_by_count":10,"area":null},{"id":"2310.06112","title":"Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach","created":"2023-10-09","cited_by_count":9,"area":null},{"id":"2502.01347","title":"Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterization","created":"2025-02-03","cited_by_count":9,"area":null},{"id":"2405.05525","title":"Ditto: Quantization-aware Secure Inference of Transformers upon MPC","created":"2024-05-09","cited_by_count":8,"area":null},{"id":"2210.05371","title":"On skip connections and normalisation layers in deep optimisation","created":"2022-10-10","cited_by_count":7,"area":null},{"id":"2305.14683","title":"On progressive sharpening, flat minima and generalisation","created":"2023-05-24","cited_by_count":7,"area":null},{"id":"2605.03601","title":"Most ReLU Networks Admit Identifiable Parameters","created":"2026-05-05","cited_by_count":6,"area":null},{"id":"2211.04589","title":"Finite Sample Identification of Wide Shallow Neural Networks with Biases","created":"2022-11-08","cited_by_count":5,"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":"2509.20829","title":"Explaining Grokking and Information Bottleneck through Neural Collapse Emergence","created":"2025-09-25","cited_by_count":4,"area":null},{"id":"2510.17506","title":"Convergence Rates for Gradient Descent on the Edge of Stability in Overparametrised Least Squares","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2210.06819","title":"Mean-field analysis for heavy ball methods: Dropout-stability, connectivity, and global convergence","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2405.07527","title":"Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models","created":"2024-05-13","cited_by_count":3,"area":null},{"id":"2410.16901","title":"Bayes without Underfitting: Fully Correlated Deep Learning Posteriors via Alternating Projections","created":"2024-10-22","cited_by_count":3,"area":null},{"id":"2508.03867","title":"Constraining the outputs of ReLU neural networks","created":"2025-08-05","cited_by_count":3,"area":null},{"id":"2509.22214","title":"A Law of Data Reconstruction for Random Features (and Beyond)","created":"2025-09-26","cited_by_count":3,"area":null},{"id":"2205.13507","title":"A Framework for Overparameterized Learning","created":"2022-05-26","cited_by_count":2,"area":null},{"id":"2405.13738","title":"Interpolation with deep neural networks with non-polynomial activations: necessary and sufficient numbers of neurons","created":"2024-05-22","cited_by_count":2,"area":null},{"id":"2212.02323","title":"Improved Convergence Guarantees for Shallow Neural Networks","created":"2022-12-05","cited_by_count":1,"area":null},{"id":"2508.19352","title":"Memorization in Graph Neural Networks","created":"2025-08-26","cited_by_count":1,"area":null},{"id":"2509.24544","title":"Quantitative convergence of trained single layer neural networks to Gaussian processes","created":"2025-09-29","cited_by_count":1,"area":null},{"id":"2603.29824","title":"Curvature-Guided LoRA: Matching Full Fine-Tuning in Function Space","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2607.08380","title":"Dynamics of Gradient Descent with Large Step Size Near a Manifold of Flat Minima","created":"2026-07-09","cited_by_count":1,"area":null},{"id":"2402.02711","title":"Architectural Strategies for the optimization of Physics-Informed Neural Networks","created":"2024-02-05","cited_by_count":0,"area":null},{"id":"2408.01374","title":"Hybrid Coordinate Descent for Efficient Neural Network Learning Using Line Search and Gradient Descent","created":"2024-08-02","cited_by_count":0,"area":null},{"id":"2512.11089","title":"TPV: Parameter Perturbations Through the Lens of Test Prediction Variance","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2606.12720","title":"On McDiarmid's Inequality under Dependence via Approximate Tensorization of Entropy","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2607.10200","title":"The Differential Neural Tangent Kernel and Its Positivity","created":"2026-07-11","cited_by_count":0,"area":null}],"next_cursor":null}