{"total":120,"rows":[{"id":"2110.06296","title":"The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks","created":"2021-10-12","cited_by_count":291,"area":null},{"id":"2202.03599","title":"Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning","created":"2022-02-08","cited_by_count":122,"area":null},{"id":"2301.12780","title":"Equivariant Architectures for Learning in Deep Weight Spaces","created":"2023-01-30","cited_by_count":107,"area":null},{"id":"2210.09221","title":"Vision Transformers provably learn spatial structure","created":"2022-10-13","cited_by_count":93,"area":null},{"id":"2110.15777","title":"GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily","created":"2021-10-29","cited_by_count":84,"area":null},{"id":"2405.14813","title":"Scalable Optimization in the Modular Norm","created":"2024-05-23","cited_by_count":68,"area":null},{"id":"2105.03361","title":"What Kinds of Functions do Deep Neural Networks Learn? Insights from Variational Spline Theory","created":"2021-05-07","cited_by_count":60,"area":null},{"id":"2305.17212","title":"Rotational Equilibrium: How Weight Decay Balances Learning Across Neural Networks","created":"2023-05-26","cited_by_count":53,"area":null},{"id":"2105.02716","title":"Noether's Learning Dynamics: Role of Symmetry Breaking in Neural Networks","created":"2021-05-06","cited_by_count":50,"area":null},{"id":"2109.09710","title":"Understanding neural networks with reproducing kernel Banach spaces","created":"2021-09-20","cited_by_count":50,"area":null},{"id":"2402.02769","title":"Learning from Teaching Regularization: Generalizable Correlations Should be Easy to Imitate","created":"2024-02-05","cited_by_count":47,"area":null},{"id":"2303.15725","title":"Solving Regularized Exp, Cosh and Sinh Regression Problems","created":"2023-03-28","cited_by_count":44,"area":null},{"id":"2109.08844","title":"Near-Minimax Optimal Estimation With Shallow ReLU Neural Networks","created":"2021-09-18","cited_by_count":41,"area":null},{"id":"2207.05931","title":"Towards understanding how momentum improves generalization in deep learning","created":"2022-07-13","cited_by_count":40,"area":null},{"id":"2201.08025","title":"Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape","created":"2022-01-20","cited_by_count":39,"area":null},{"id":"2306.06179","title":"Hidden symmetries of ReLU networks","created":"2023-06-09","cited_by_count":39,"area":null},{"id":"2511.00674","title":"Isotropic Curvature Model for Understanding Deep Learning Optimization: Is Gradient Orthogonalization Optimal?","created":"2025-11-01","cited_by_count":37,"area":null},{"id":"2506.13018","title":"Symmetry in Neural Network Parameter Spaces","created":"2025-06-16","cited_by_count":36,"area":null},{"id":"2210.17216","title":"Symmetries, flat minima, and the conserved quantities of gradient flow","created":"2022-10-31","cited_by_count":35,"area":null},{"id":"2201.11968","title":"Training invariances and the low-rank phenomenon: beyond linear networks","created":"2022-01-28","cited_by_count":33,"area":null},{"id":"2205.10637","title":"Symmetry Teleportation for Accelerated Optimization","created":"2022-05-21","cited_by_count":25,"area":null},{"id":"2306.08321","title":"Nonparametric regression using over-parameterized shallow ReLU neural networks","created":"2023-06-14","cited_by_count":24,"area":null},{"id":"2406.10685","title":"Scale Equivariant Graph Metanetworks","created":"2024-06-15","cited_by_count":24,"area":null},{"id":"2305.13404","title":"Improving Convergence and Generalization Using Parameter Symmetries","created":"2023-05-22","cited_by_count":23,"area":null},{"id":"2201.09418","title":"Approximation bounds for norm constrained neural networks with applications to regression and GANs","created":"2022-01-24","cited_by_count":22,"area":null},{"id":"2405.20231","title":"The Empirical Impact of Neural Parameter Symmetries, or Lack Thereof","created":"2024-05-30","cited_by_count":21,"area":null},{"id":"2107.09370","title":"An Embedding of ReLU Networks and an Analysis of their Identifiability","created":"2021-07-20","cited_by_count":20,"area":null},{"id":"2303.09732","title":"Rethinking White-Box Watermarks on Deep Learning Models under Neural Structural Obfuscation","created":"2023-03-17","cited_by_count":20,"area":"safety"},{"id":"2309.13658","title":"Fantastic Generalization Measures are Nowhere to be Found","created":"2023-09-24","cited_by_count":19,"area":null},{"id":"2410.22069","title":"Flavors of Margin: Implicit Bias of Steepest Descent in Homogeneous Neural Networks","created":"2024-10-29","cited_by_count":18,"area":null},{"id":"2409.11697","title":"Monomial Matrix Group Equivariant Neural Functional Networks","created":"2024-09-18","cited_by_count":17,"area":null},{"id":"2302.07384","title":"The Geometry of Neural Nets' Parameter Spaces Under Reparametrization","created":"2023-02-14","cited_by_count":16,"area":null},{"id":"2502.00264","title":"Beyond the Permutation Symmetry of Transformers: The Role of Rotation for Model Fusion","created":"2025-02-01","cited_by_count":16,"area":null},{"id":"2110.09548","title":"Path Regularization: A Convexity and Sparsity Inducing Regularization for Parallel ReLU Networks","created":"2021-10-18","cited_by_count":14,"area":null},{"id":"2307.00908","title":"Quantum Machine Learning on Near-Term Quantum Devices: Current State of Supervised and Unsupervised Techniques for Real-World Applications","created":"2023-07-03","cited_by_count":14,"area":null},{"id":"2211.01258","title":"Instance-Dependent Generalization Bounds via Optimal Transport","created":"2022-11-02","cited_by_count":13,"area":null},{"id":"2205.08099","title":"Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey","created":"2022-05-17","cited_by_count":12,"area":null},{"id":"2206.07424","title":"Local Identifiability of Deep ReLU Neural Networks: the Theory","created":"2022-06-15","cited_by_count":12,"area":null},{"id":"2209.06788","title":"Small Transformers Compute Universal Metric Embeddings","created":"2022-09-14","cited_by_count":12,"area":null},{"id":"2310.08287","title":"A Symmetry-Aware Exploration of Bayesian Neural Network Posteriors","created":"2023-10-12","cited_by_count":12,"area":null},{"id":"2602.16340","title":"The Implicit Bias of Adam and Muon on Smooth Homogeneous Neural Networks","created":"2026-02-18","cited_by_count":11,"area":null},{"id":"2104.04874","title":"SGD Implicitly Regularizes Generalization Error","created":"2021-04-10","cited_by_count":10,"area":null},{"id":"2410.14802","title":"Implicit Regularization of Sharpness-Aware Minimization for Scale-Invariant Problems","created":"2024-10-18","cited_by_count":10,"area":null},{"id":"2401.12236","title":"The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness","created":"2024-01-19","cited_by_count":9,"area":null},{"id":"2410.24058","title":"Natural gradient and parameter estimation for quantum Boltzmann machines","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2107.02550","title":"Universal approximation and model compression for radial neural networks","created":"2021-07-06","cited_by_count":7,"area":null},{"id":"2209.15208","title":"Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel","created":"2022-09-30","cited_by_count":7,"area":null},{"id":"2307.15772","title":"Weighted variation spaces and approximation by shallow ReLU networks","created":"2023-07-28","cited_by_count":7,"area":null},{"id":"2402.02362","title":"Unification of Symmetries Inside Neural Networks: Transformer, Feedforward and Neural ODE","created":"2024-02-04","cited_by_count":7,"area":null},{"id":"2402.13285","title":"Leveraging PAC-Bayes Theory and Gibbs Distributions for Generalization Bounds with Complexity Measures","created":"2024-02-19","cited_by_count":7,"area":null},{"id":"2403.17520","title":"Boosting Adversarial Training via Fisher-Rao Norm-based Regularization","created":"2024-03-26","cited_by_count":7,"area":null},{"id":"2403.17592","title":"On the Benefits of Over-parameterization for Out-of-Distribution Generalization","created":"2024-03-26","cited_by_count":7,"area":null},{"id":"2103.09716","title":"Quantitative Performance Assessment of CNN Units via Topological Entropy Calculation","created":"2021-03-17","cited_by_count":6,"area":null},{"id":"2201.09209","title":"Weight Expansion: A New Perspective on Dropout and Generalization","created":"2022-01-23","cited_by_count":6,"area":null},{"id":"2205.00199","title":"Cracking White-box DNN Watermarks via Invariant Neuron Transforms","created":"2022-04-30","cited_by_count":6,"area":null},{"id":"2410.14837","title":"Topological obstruction to the training of shallow ReLU neural networks","created":"2024-10-18","cited_by_count":6,"area":null},{"id":"2602.00693","title":"Topology and Geometry of the Learning Space of ReLU Networks: Connectivity and Singularities","created":"2026-01-31","cited_by_count":6,"area":null},{"id":"2602.19799","title":"Path-conditioned training: a principled way to rescale ReLU neural networks","created":"2026-02-23","cited_by_count":6,"area":null},{"id":"2108.01110","title":"Batch Normalization Preconditioning for Neural Network Training","created":"2021-08-02","cited_by_count":5,"area":null},{"id":"2406.02529","title":"ReLUs Are Sufficient for Learning Implicit Neural Representations","created":"2024-06-04","cited_by_count":5,"area":null},{"id":"2601.05052","title":"DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights","created":"2026-01-08","cited_by_count":5,"area":"diffusion"},{"id":"2605.03769","title":"Nora: Normalized Orthogonal Row Alignment for Scalable Matrix Optimizer","created":"2026-05-05","cited_by_count":5,"area":null},{"id":"2101.02916","title":"Accelerating Training of Batch Normalization: A Manifold Perspective","created":"2021-01-08","cited_by_count":4,"area":null},{"id":"2112.06209","title":"Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation","created":"2021-12-12","cited_by_count":4,"area":null},{"id":"2207.02093","title":"Predicting Out-of-Domain Generalization with Neighborhood Invariance","created":"2022-07-05","cited_by_count":4,"area":null},{"id":"2207.12773","title":"Quiver neural networks","created":"2022-07-26","cited_by_count":4,"area":null},{"id":"2305.11417","title":"Exploring the Complexity of Deep Neural Networks through Functional Equivalence","created":"2023-05-19","cited_by_count":4,"area":null},{"id":"2410.01483","title":"Foldable SuperNets: Scalable Merging of Transformers with Different Initializations and Tasks","created":"2024-10-02","cited_by_count":4,"area":null},{"id":"2411.17182","title":"An In-depth Investigation of Sparse Rate Reduction in Transformer-like Models","created":"2024-11-26","cited_by_count":4,"area":null},{"id":"2110.00199","title":"Perturbated Gradients Updating within Unit Space for Deep Learning","created":"2021-10-01","cited_by_count":3,"area":null},{"id":"2211.07816","title":"Quantifying the Impact of Label Noise on Federated Learning","created":"2022-11-15","cited_by_count":3,"area":null},{"id":"2304.09221","title":"Convergence of stochastic gradient descent under a local Lojasiewicz condition for deep neural networks","created":"2023-04-18","cited_by_count":3,"area":null},{"id":"2403.00946","title":"Fine-tuning with Very Large Dropout","created":"2024-03-01","cited_by_count":3,"area":null},{"id":"2403.10459","title":"Understanding the Double Descent Phenomenon in Deep Learning","created":"2024-03-15","cited_by_count":3,"area":null},{"id":"2406.01755","title":"Sparser, Better, Deeper, Stronger: Improving Sparse Training with Exact Orthogonal Initialization","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2408.04065","title":"Do Sharpness-based Optimizers Improve Generalization in Medical Image Analysis?","created":"2024-08-07","cited_by_count":3,"area":null},{"id":"2110.12259","title":"In Search of Probeable Generalization Measures","created":"2021-10-23","cited_by_count":2,"area":null},{"id":"2210.03069","title":"PathProx: A Proximal Gradient Algorithm for Weight Decay Regularized Deep Neural Networks","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2311.08745","title":"Using Stochastic Gradient Descent to Smooth Nonconvex Functions: Analysis of Implicit Graduated Optimization","created":"2023-11-15","cited_by_count":2,"area":null},{"id":"2504.15399","title":"Improving Learning to Optimize Using Parameter Symmetries","created":"2025-04-21","cited_by_count":2,"area":null},{"id":"2102.03497","title":"Weight Rescaling: Effective and Robust Regularization for Deep Neural Networks with Batch Normalization","created":"2021-02-06","cited_by_count":1,"area":null},{"id":"2106.10653","title":"Practical Assessment of Generalization Performance Robustness for Deep Networks via Contrastive Examples","created":"2021-06-20","cited_by_count":1,"area":null},{"id":"2203.15972","title":"Higher-Order Generalization Bounds: Learning Deep Probabilistic Programs via PAC-Bayes Objectives","created":"2022-03-30","cited_by_count":1,"area":null},{"id":"2310.00873","title":"Deep Neural Networks Tend To Extrapolate Predictably","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2311.15404","title":"Applying statistical learning theory to deep learning","created":"2023-11-26","cited_by_count":1,"area":null},{"id":"2403.03362","title":"Level Set Teleportation: An Optimization Perspective","created":"2024-03-05","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":"2408.05629","title":"Quantum-secure multiparty deep learning","created":"2024-08-10","cited_by_count":1,"area":null},{"id":"2502.00744","title":"CoNNect: Connectivity-Based Regularization for Structural Pruning","created":"2025-02-02","cited_by_count":1,"area":null},{"id":"2502.17391","title":"The Empirical Impact of Reducing Symmetries on the Performance of Deep Ensembles and MoE","created":"2025-02-24","cited_by_count":1,"area":"moe"},{"id":"2507.04683","title":"Recovering Plasticity of Neural Networks via Soft Weight Rescaling","created":"2025-07-07","cited_by_count":1,"area":null},{"id":"2509.21181","title":"Closed-form $\\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\\ell_p$ bias","created":"2025-09-25","cited_by_count":1,"area":null},{"id":"2601.06730","title":"Why are there many equally good models? An Anatomy of the Rashomon Effect","created":"2026-01-11","cited_by_count":1,"area":null},{"id":"2601.18420","title":"Gradient Regularized Natural Gradients","created":"2026-01-26","cited_by_count":1,"area":null},{"id":"2605.16134","title":"Navigating Potholes with Geometry-Aware Sharpness Minimization","created":"2026-05-15","cited_by_count":1,"area":null},{"id":"2608.06766","title":"Hidden Gauge Controls Feature Specialization in ReLU Networks","created":"2026-08-07","cited_by_count":1,"area":null},{"id":"2609.38998","title":"Not all solutions are created equal: An analytical dissociation of functional and representational similarity in deep linear neural networks","created":"2026-09-30","cited_by_count":1,"area":null},{"id":"2106.02793","title":"Solving hybrid machine learning tasks by traversing weight space geodesics","created":"2021-06-05","cited_by_count":0,"area":null},{"id":"2203.10973","title":"A Local Convergence Theory for the Stochastic Gradient Descent Method in Non-Convex Optimization With Non-isolated Local Minima","created":"2022-03-21","cited_by_count":0,"area":null},{"id":"2310.17378","title":"Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle","created":"2023-10-26","cited_by_count":0,"area":null}],"next_cursor":"100"}