{"total":134,"rows":[{"id":"2210.12067","title":"Efficient Dataset Distillation Using Random Feature Approximation","created":"2022-10-21","cited_by_count":111,"area":null},{"id":"2102.01063","title":"Zen-NAS: A Zero-Shot NAS for High-Performance Deep Image Recognition","created":"2021-02-01","cited_by_count":73,"area":null},{"id":"2105.03703","title":"Tensor Programs IIb: Architectural Universality of Neural Tangent Kernel Training Dynamics","created":"2021-05-08","cited_by_count":57,"area":null},{"id":"2102.02410","title":"A Local Convergence Theory for Mildly Over-Parameterized Two-Layer Neural Network","created":"2021-02-04","cited_by_count":47,"area":null},{"id":"2302.10322","title":"Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation","created":"2023-02-20","cited_by_count":45,"area":null},{"id":"2107.01562","title":"Random Neural Networks in the Infinite Width Limit as Gaussian Processes","created":"2021-07-04","cited_by_count":44,"area":null},{"id":"2103.01210","title":"Quantifying the Benefit of Using Differentiable Learning over Tangent Kernels","created":"2021-03-01","cited_by_count":41,"area":null},{"id":"2104.04244","title":"How rotational invariance of common kernels prevents generalization in high dimensions","created":"2021-04-09","cited_by_count":32,"area":null},{"id":"2106.07880","title":"Scaling Neural Tangent Kernels via Sketching and Random Features","created":"2021-06-15","cited_by_count":32,"area":null},{"id":"2102.10032","title":"Approximation and Learning with Deep Convolutional Models: a Kernel Perspective","created":"2021-02-19","cited_by_count":31,"area":null},{"id":"2111.14482","title":"High Quality Segmentation for Ultra High-resolution Images","created":"2021-11-29","cited_by_count":31,"area":null},{"id":"2203.08120","title":"Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers","created":"2022-03-15","cited_by_count":28,"area":null},{"id":"2105.10585","title":"Properties of the After Kernel","created":"2021-05-21","cited_by_count":27,"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":"2211.07844","title":"Characterizing the Spectrum of the NTK via a Power Series Expansion","created":"2022-11-15","cited_by_count":22,"area":null},{"id":"2302.06210","title":"Precise Asymptotic Analysis of Deep Random Feature Models","created":"2023-02-13","cited_by_count":22,"area":null},{"id":"2109.06099","title":"Uniform Generalization Bounds for Overparameterized Neural Networks","created":"2021-09-13","cited_by_count":21,"area":null},{"id":"2109.09304","title":"Deformed semicircle law and concentration of nonlinear random matrices for ultra-wide neural networks","created":"2021-09-20","cited_by_count":21,"area":null},{"id":"2111.08308","title":"Learning with convolution and pooling operations in kernel methods","created":"2021-11-16","cited_by_count":21,"area":null},{"id":"2206.02626","title":"Infinite Recommendation Networks: A Data-Centric Approach","created":"2022-06-03","cited_by_count":20,"area":null},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"area":null},{"id":"2101.02533","title":"Towards Understanding Learning in Neural Networks with Linear Teachers","created":"2021-01-07","cited_by_count":19,"area":null},{"id":"2102.08668","title":"Non-asymptotic approximations of neural networks by Gaussian processes","created":"2021-02-17","cited_by_count":19,"area":null},{"id":"2205.06798","title":"Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime","created":"2022-05-13","cited_by_count":19,"area":null},{"id":"2209.04121","title":"Fast Neural Kernel Embeddings for General Activations","created":"2022-09-09","cited_by_count":18,"area":null},{"id":"2305.18512","title":"A Rainbow in Deep Network Black Boxes","created":"2023-05-29","cited_by_count":18,"area":null},{"id":"2108.00131","title":"Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks","created":"2021-07-31","cited_by_count":17,"area":null},{"id":"2112.05611","title":"Eigenspace Restructuring: a Principle of Space and Frequency in Neural Networks","created":"2021-12-10","cited_by_count":17,"area":null},{"id":"2309.00570","title":"Mechanism of feature learning in convolutional neural networks","created":"2023-09-01","cited_by_count":17,"area":null},{"id":"2107.10370","title":"Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II","created":"2021-07-21","cited_by_count":16,"area":null},{"id":"2208.01003","title":"What Can Be Learnt With Wide Convolutional Neural Networks?","created":"2022-08-01","cited_by_count":16,"area":null},{"id":"2107.10209","title":"Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations","created":"2021-07-21","cited_by_count":15,"area":null},{"id":"2107.12530","title":"Convergence of Deep ReLU Networks","created":"2021-07-27","cited_by_count":15,"area":null},{"id":"2109.11700","title":"Untrained Graph Neural Networks for Denoising","created":"2021-09-24","cited_by_count":15,"area":null},{"id":"2110.12231","title":"Learning curves for Gaussian process regression with power-law priors and targets","created":"2021-10-23","cited_by_count":15,"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":"2101.06309","title":"Fundamental Tradeoffs in Distributionally Adversarial Training","created":"2021-01-15","cited_by_count":12,"area":null},{"id":"2106.03186","title":"Reverse Engineering the Neural Tangent Kernel","created":"2021-06-06","cited_by_count":12,"area":null},{"id":"2204.14126","title":"Wide and Deep Neural Networks Achieve Optimality for Classification","created":"2022-04-29","cited_by_count":12,"area":null},{"id":"2211.14810","title":"A Kernel Perspective of Skip Connections in Convolutional Networks","created":"2022-11-27","cited_by_count":12,"area":null},{"id":"2312.12474","title":"Principled Weight Initialisation for Input-Convex Neural Networks","created":"2023-12-19","cited_by_count":12,"area":null},{"id":"2101.07833","title":"Implicit Bias of Linear RNNs","created":"2021-01-19","cited_by_count":11,"area":null},{"id":"2203.11864","title":"On the (Non-)Robustness of Two-Layer Neural Networks in Different Learning Regimes","created":"2022-03-22","cited_by_count":11,"area":null},{"id":"2307.06565","title":"Efficient SGD Neural Network Training via Sublinear Activated Neuron Identification","created":"2023-07-13","cited_by_count":11,"area":null},{"id":"2205.13814","title":"Global Convergence of Over-parameterized Deep Equilibrium Models","created":"2022-05-27","cited_by_count":10,"area":null},{"id":"2305.09779","title":"A Scalable Walsh-Hadamard Regularizer to Overcome the Low-degree Spectral Bias of Neural Networks","created":"2023-05-16","cited_by_count":10,"area":null},{"id":"2309.08044","title":"How many Neurons do we need? A refined Analysis for Shallow Networks trained with Gradient Descent","created":"2023-09-14","cited_by_count":10,"area":null},{"id":"2203.14328","title":"On the Neural Tangent Kernel Analysis of Randomly Pruned Neural Networks","created":"2022-03-27","cited_by_count":9,"area":null},{"id":"2210.06088","title":"Annihilation of Spurious Minima in Two-Layer ReLU Networks","created":"2022-10-12","cited_by_count":9,"area":null},{"id":"2307.14531","title":"Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum","created":"2023-07-26","cited_by_count":9,"area":null},{"id":"2310.11891","title":"A Hyperparameter Study for Quantum Kernel Methods","created":"2023-10-18","cited_by_count":9,"area":null},{"id":"2411.08798","title":"Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence","created":"2024-11-13","cited_by_count":9,"area":null},{"id":"2110.12141","title":"Rethinking Neural vs. Matrix-Factorization Collaborative Filtering: the Theoretical Perspectives","created":"2021-10-23","cited_by_count":8,"area":null},{"id":"2201.08082","title":"Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions","created":"2022-01-20","cited_by_count":8,"area":null},{"id":"2306.16932","title":"A Quantitative Functional Central Limit Theorem for Shallow Neural Networks","created":"2023-06-29","cited_by_count":8,"area":null},{"id":"2309.07452","title":"Is Solving Graph Neural Tangent Kernel Equivalent to Training Graph Neural Network?","created":"2023-09-14","cited_by_count":8,"area":null},{"id":"2311.01644","title":"Should Under-parameterized Student Networks Copy or Average Teacher Weights?","created":"2023-11-03","cited_by_count":8,"area":null},{"id":"2403.02251","title":"A prediction rigidity formalism for low-cost uncertainties in trained neural networks","created":"2024-03-04","cited_by_count":8,"area":null},{"id":"2410.06300","title":"SHAP values via sparse Fourier representation","created":"2024-10-08","cited_by_count":8,"area":null},{"id":"2209.07736","title":"Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study","created":"2022-09-16","cited_by_count":7,"area":null},{"id":"2211.06077","title":"Overparameterized random feature regression with nearly orthogonal data","created":"2022-11-11","cited_by_count":7,"area":null},{"id":"2103.06875","title":"For Manifold Learning, Deep Neural Networks can be Locality Sensitive Hash Functions","created":"2021-03-11","cited_by_count":6,"area":null},{"id":"2104.01351","title":"Random Features for the Neural Tangent Kernel","created":"2021-04-03","cited_by_count":6,"area":null},{"id":"2109.14142","title":"On the Provable Generalization of Recurrent Neural Networks","created":"2021-09-29","cited_by_count":6,"area":null},{"id":"2206.00099","title":"Provably and Practically Efficient Neural Contextual Bandits","created":"2022-05-31","cited_by_count":6,"area":null},{"id":"2207.04612","title":"Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm","created":"2022-07-11","cited_by_count":6,"area":null},{"id":"2411.15669","title":"Implicit High-Order Moment Tensor Estimation and Learning Latent Variable Models","created":"2024-11-23","cited_by_count":6,"area":null},{"id":"2102.07238","title":"Double-descent curves in neural networks: a new perspective using Gaussian processes","created":"2021-02-14","cited_by_count":5,"area":null},{"id":"2202.01944","title":"Learning Representation from Neural Fisher Kernel with Low-rank Approximation","created":"2022-02-04","cited_by_count":5,"area":null},{"id":"2212.13848","title":"Learning Lipschitz Functions by GD-trained Shallow Overparameterized ReLU Neural Networks","created":"2022-12-28","cited_by_count":5,"area":null},{"id":"2302.03459","title":"On the relationship between multivariate splines and infinitely-wide neural networks","created":"2023-02-07","cited_by_count":5,"area":null},{"id":"2306.01692","title":"Uniform Convergence of Deep Neural Networks with Lipschitz Continuous Activation Functions and Variable Widths","created":"2023-06-02","cited_by_count":5,"area":null},{"id":"2307.01177","title":"Neural Hilbert Ladders: Multi-Layer Neural Networks in Function Space","created":"2023-07-03","cited_by_count":5,"area":null},{"id":"2506.00846","title":"Infinite-Width Limit of a Single Attention Layer: Analysis via Tensor Programs","created":"2025-06-01","cited_by_count":5,"area":null},{"id":"2104.05878","title":"On the validity of kernel approximations for orthogonally-initialized neural networks","created":"2021-04-13","cited_by_count":4,"area":null},{"id":"2112.00980","title":"Trap of Feature Diversity in the Learning of MLPs","created":"2021-12-02","cited_by_count":4,"area":null},{"id":"2206.01332","title":"Optimal Activation Functions for the Random Features Regression Model","created":"2022-05-31","cited_by_count":4,"area":null},{"id":"2210.00346","title":"Behind the Scenes of Gradient Descent: A Trajectory Analysis via Basis Function Decomposition","created":"2022-10-01","cited_by_count":4,"area":null},{"id":"2302.05797","title":"Global Convergence Rate of Deep Equilibrium Models with General Activations","created":"2023-02-11","cited_by_count":4,"area":null},{"id":"2307.12840","title":"Efficiently Learning One-Hidden-Layer ReLU Networks via Schur Polynomials","created":"2023-07-24","cited_by_count":4,"area":null},{"id":"2408.13591","title":"Optimal Kernel Quantile Learning with Random Features","created":"2024-08-24","cited_by_count":4,"area":null},{"id":"2103.15213","title":"A Temporal Kernel Approach for Deep Learning with Continuous-time Information","created":"2021-03-28","cited_by_count":3,"area":null},{"id":"2111.03972","title":"Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis","created":"2021-11-06","cited_by_count":3,"area":null},{"id":"2204.07261","title":"Convergence and Implicit Regularization Properties of Gradient Descent for Deep Residual Networks","created":"2022-04-14","cited_by_count":3,"area":null},{"id":"2405.09541","title":"Spectral complexity of deep neural networks","created":"2024-05-15","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":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2506.22429","title":"Beyond ReLU: How Activations Affect Neural Kernels and Random Wide Networks","created":"2025-06-27","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":"2101.02333","title":"Infinitely Wide Tensor Networks as Gaussian Process","created":"2021-01-07","cited_by_count":2,"area":null},{"id":"2106.05710","title":"DNN-Based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel","created":"2021-06-10","cited_by_count":2,"area":null},{"id":"2107.00364","title":"Implicit Acceleration and Feature Learning in Infinitely Wide Neural Networks with Bottlenecks","created":"2021-07-01","cited_by_count":2,"area":null},{"id":"2201.04669","title":"On neural network kernels and the storage capacity problem","created":"2022-01-12","cited_by_count":2,"area":null},{"id":"2305.16508","title":"Most Neural Networks Are Almost Learnable","created":"2023-05-25","cited_by_count":2,"area":null},{"id":"2404.17461","title":"Multi-layer random features and the approximation power of neural networks","created":"2024-04-26","cited_by_count":2,"area":null},{"id":"2407.19353","title":"Spring-block theory of feature learning in deep neural networks","created":"2024-07-28","cited_by_count":2,"area":null},{"id":"2508.10490","title":"On the Complexity-Faithfulness Trade-off of Gradient-Based Explanations","created":"2025-08-14","cited_by_count":2,"area":null},{"id":"2601.00455","title":"Deep Networks Learn Deep Hierarchical Models","created":"2026-01-01","cited_by_count":2,"area":null},{"id":"2103.11558","title":"Weighted Neural Tangent Kernel: A Generalized and Improved Network-Induced Kernel","created":"2021-03-22","cited_by_count":1,"area":null}],"next_cursor":"100"}