{"total":30,"rows":[{"id":"2107.13034","title":"Dataset Distillation with Infinitely Wide Convolutional Networks","created":"2021-07-27","cited_by_count":218,"area":null},{"id":"2107.03144","title":"Neural Contextual Bandits without Regret","created":"2021-07-07","cited_by_count":40,"area":null},{"id":"2302.02774","title":"The SSL Interplay: Augmentations, Inductive Bias, and Generalization","created":"2023-02-06","cited_by_count":40,"area":null},{"id":"2307.11353","title":"What can a Single Attention Layer Learn? A Study Through the Random Features Lens","created":"2023-07-21","cited_by_count":40,"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":"2305.08404","title":"Theoretical Analysis of Inductive Biases in Deep Convolutional Networks","created":"2023-05-15","cited_by_count":21,"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":"2203.09255","title":"On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels","created":"2022-03-17","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":"2106.07148","title":"On the Sample Complexity of Learning under Invariance and Geometric Stability","created":"2021-06-14","cited_by_count":13,"area":null},{"id":"2402.05173","title":"Towards Understanding Inductive Bias in Transformers: A View From Infinity","created":"2024-02-07","cited_by_count":13,"area":null},{"id":"2206.11124","title":"A view of mini-batch SGD via generating functions: conditions of convergence, phase transitions, benefit from negative momenta","created":"2022-06-22","cited_by_count":11,"area":null},{"id":"2301.07605","title":"Strong inductive biases provably prevent harmless interpolation","created":"2023-01-18","cited_by_count":10,"area":null},{"id":"2202.00992","title":"Tight Convergence Rate Bounds for Optimization Under Power Law Spectral Conditions","created":"2022-02-02","cited_by_count":9,"area":null},{"id":"2211.14047","title":"On the Universal Approximation Property of Deep Fully Convolutional Neural Networks","created":"2022-11-25","cited_by_count":7,"area":null},{"id":"2404.10727","title":"How Deep Networks Learn Sparse and Hierarchical Data: the Sparse Random Hierarchy Model","created":"2024-04-16","cited_by_count":7,"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":"2211.00227","title":"Transfer Learning with Kernel Methods","created":"2022-11-01","cited_by_count":5,"area":null},{"id":"2405.09541","title":"Spectral complexity of deep neural networks","created":"2024-05-15","cited_by_count":3,"area":null},{"id":"2406.02663","title":"Demystifying Spectral Bias on Real-World Data","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2209.11740","title":"On the Shift Invariance of Max Pooling Feature Maps in Convolutional Neural Networks","created":"2022-09-19","cited_by_count":2,"area":null},{"id":"2303.15948","title":"Sparse Gaussian Processes with Spherical Harmonic Features Revisited","created":"2023-03-28","cited_by_count":2,"area":null},{"id":"2410.04228","title":"SGD with memory: fundamental properties and stochastic acceleration","created":"2024-10-05","cited_by_count":2,"area":null},{"id":"2111.02155","title":"A Johnson--Lindenstrauss Framework for Randomly Initialized CNNs","created":"2021-11-03","cited_by_count":0,"area":null},{"id":"2307.02693","title":"Kernels, Data & Physics","created":"2023-07-05","cited_by_count":0,"area":null},{"id":"2408.04277","title":"Stability Analysis of Equivariant Convolutional Representations Through The Lens of Equivariant Multi-layered CKNs","created":"2024-08-08","cited_by_count":0,"area":null},{"id":"2512.12898","title":"Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks","created":"2025-12-15","cited_by_count":0,"area":null},{"id":"2604.02887","title":"Lipschitz bounds for integral kernels","created":"2026-04-03","cited_by_count":0,"area":null},{"id":"2609.33715","title":"Theory Guided and Interpretable Neural Operator Design for Partial Differential Equation Learning","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}