{"total":33,"rows":[{"id":"2309.16620","title":"Depthwise Hyperparameter Transfer in Residual Networks: Dynamics and Scaling Limit","created":"2023-09-28","cited_by_count":66,"area":null},{"id":"2306.17759","title":"The Shaped Transformer: Attention Models in the Infinite Depth-and-Width Limit","created":"2023-06-30","cited_by_count":47,"area":null},{"id":"2304.03408","title":"Dynamics of Finite Width Kernel and Prediction Fluctuations in Mean Field Neural Networks","created":"2023-04-06","cited_by_count":43,"area":null},{"id":"2206.02768","title":"The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization","created":"2022-06-06","cited_by_count":41,"area":null},{"id":"2305.02299","title":"Dynamic Sparse Training with Structured Sparsity","created":"2023-05-03","cited_by_count":38,"area":null},{"id":"2202.00553","title":"Neural Tangent Kernel Beyond the Infinite-Width Limit: Effects of Depth and Initialization","created":"2022-02-01","cited_by_count":30,"area":null},{"id":"2106.06615","title":"Precise characterization of the prior predictive distribution of deep ReLU networks","created":"2021-06-11","cited_by_count":28,"area":null},{"id":"2305.14077","title":"Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension","created":"2023-05-23","cited_by_count":26,"area":null},{"id":"2402.17457","title":"Super Consistency of Neural Network Landscapes and Learning Rate Transfer","created":"2024-02-27","cited_by_count":23,"area":null},{"id":"2210.00688","title":"On the infinite-depth limit of finite-width neural networks","created":"2022-10-03","cited_by_count":22,"area":null},{"id":"2302.00453","title":"Width and Depth Limits Commute in Residual Networks","created":"2023-02-01","cited_by_count":21,"area":null},{"id":"2210.04909","title":"Meta-Principled Family of Hyperparameter Scaling Strategies","created":"2022-10-10","cited_by_count":19,"area":null},{"id":"2310.10767","title":"Wide Neural Networks as Gaussian Processes: Lessons from Deep Equilibrium Models","created":"2023-10-16","cited_by_count":14,"area":null},{"id":"2305.18399","title":"On the impact of activation and normalization in obtaining isometric embeddings at initialization","created":"2023-05-28","cited_by_count":13,"area":null},{"id":"2311.18718","title":"The Feature Speed Formula: a flexible approach to scale hyper-parameters of deep neural networks","created":"2023-11-30","cited_by_count":11,"area":null},{"id":"2108.02316","title":"Deep Stable neural networks: large-width asymptotics and convergence rates","created":"2021-08-02","cited_by_count":8,"area":null},{"id":"2310.14062","title":"On the Neural Tangent Kernel of Equilibrium Models","created":"2023-10-21","cited_by_count":7,"area":null},{"id":"2410.10744","title":"Adversarially Robust Out-of-Distribution Detection Using Lyapunov-Stabilized Embeddings","created":"2024-10-14","cited_by_count":6,"area":null},{"id":"2410.16201","title":"Theoretical Limitations of Ensembles in the Age of Overparameterization","created":"2024-10-21","cited_by_count":6,"area":null},{"id":"2207.09408","title":"Bounding generalization error with input compression: An empirical study with infinite-width networks","created":"2022-07-19","cited_by_count":4,"area":null},{"id":"2310.12079","title":"Differential Equation Scaling Limits of Shaped and Unshaped Neural Networks","created":"2023-10-18","cited_by_count":4,"area":null},{"id":"2403.09889","title":"Generalization of Scaled Deep ResNets in the Mean-Field Regime","created":"2024-03-14","cited_by_count":3,"area":null},{"id":"2511.01734","title":"A Proof of Learning Rate Transfer under $\\mu$P","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2205.13076","title":"On Bridging the Gap between Mean Field and Finite Width in Deep Random Neural Networks with Batch Normalization","created":"2022-05-25","cited_by_count":2,"area":null},{"id":"2505.19827","title":"Revisiting Glorot Initialization for Long-Range Linear Recurrences","created":"2025-05-26","cited_by_count":2,"area":null},{"id":"2506.16283","title":"Random feature approximation for general spectral methods","created":"2025-06-19","cited_by_count":2,"area":null},{"id":"2310.01683","title":"Commutative Width and Depth Scaling in Deep Neural Networks","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2603.00971","title":"Random Features for Operator-Valued Kernels: Bridging Kernel Methods and Neural Operators","created":"2026-03-01","cited_by_count":1,"area":null},{"id":"2210.02411","title":"Dynamical Isometry for Residual Networks","created":"2022-10-05","cited_by_count":0,"area":null},{"id":"2305.09179","title":"Ortho-ODE: Enhancing Robustness and of Neural ODEs against Adversarial Attacks","created":"2023-05-16","cited_by_count":0,"area":null},{"id":"2308.15434","title":"Random feature approximation for general spectral methods","created":"2023-08-29","cited_by_count":0,"area":null},{"id":"2604.15742","title":"Collective Kernel EFT for Pre-activation ResNets","created":"2026-04-17","cited_by_count":0,"area":null},{"id":"2606.30831","title":"Geometric Dyson Brownian Motions and the Free Log-Normal Limit for a Non-Square Gaussian Matrix Product","created":"2026-06-29","cited_by_count":0,"area":null}],"next_cursor":null}