{"list":"other","total":51,"rows":[{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1309.5549","title":"Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming","created":"2013-09-22","cited_by_count":724},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"1905.10018","title":"Momentum-Based Variance Reduction in Non-Convex SGD","created":"2019-05-24","cited_by_count":306},{"id":"1807.01695","title":"SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path Integrated Differential Estimator","created":"2018-07-04","cited_by_count":287},{"id":"1703.00102","title":"SARAH: A Novel Method for Machine Learning Problems Using Stochastic Recursive Gradient","created":"2017-03-01","cited_by_count":220},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"1603.06160","title":"Stochastic Variance Reduction for Nonconvex Optimization","created":"2016-03-19","cited_by_count":145},{"id":"2006.04780","title":"Lorentz Group Equivariant Neural Network for Particle Physics","created":"2020-06-08","cited_by_count":121},{"id":"2307.08423","title":"Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems","created":"2023-07-17","cited_by_count":121},{"id":"2008.10898","title":"PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization","created":"2020-08-25","cited_by_count":119},{"id":"2006.05988","title":"Random Reshuffling: Simple Analysis with Vast Improvements","created":"2020-06-10","cited_by_count":115},{"id":"2211.06489","title":"Equivariance with Learned Canonicalization Functions","created":"2022-11-11","cited_by_count":109},{"id":"1803.06084","title":"A Kernel Theory of Modern Data Augmentation","created":"2018-03-16","cited_by_count":106},{"id":"2102.10333","title":"Provably Strict Generalisation Benefit for Equivariant Models","created":"2021-02-20","cited_by_count":91},{"id":"2102.13219","title":"Learning with invariances in random features and kernel models","created":"2021-02-25","cited_by_count":89},{"id":"2109.07103","title":"Automatic Symmetry Discovery with Lie Algebra Convolutional Network","created":"2021-09-15","cited_by_count":83},{"id":"2112.01388","title":"Residual Pathway Priors for Soft Equivariance Constraints","created":"2021-12-02","cited_by_count":75},{"id":"2006.06946","title":"SGD with shuffling: optimal rates without component convexity and large epoch requirements","created":"2020-06-12","cited_by_count":58},{"id":"2204.11371","title":"Learning Symmetric Embeddings for Equivariant World Models","created":"2022-04-24","cited_by_count":54},{"id":"2402.16077","title":"Equivariant Frames and the Impossibility of Continuous Canonicalization","created":"2024-02-25","cited_by_count":43},{"id":"2202.10638","title":"Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations","created":"2022-02-22","cited_by_count":42},{"id":"2302.00236","title":"Generative Adversarial Symmetry Discovery","created":"2023-02-01","cited_by_count":42},{"id":"2204.07178","title":"Relaxing Equivariance Constraints with Non-stationary Continuous Filters","created":"2022-04-14","cited_by_count":41},{"id":"2202.12230","title":"Sample Efficiency of Data Augmentation Consistency Regularization","created":"2022-02-24","cited_by_count":28},{"id":"2303.14269","title":"The Exact Sample Complexity Gain from Invariances for Kernel Regression","created":"2023-03-24","cited_by_count":28},{"id":"2106.05121","title":"Grounding inductive biases in natural images:invariance stems from variations in data","created":"2021-06-09","cited_by_count":27},{"id":"2405.18378","title":"A Canonicalization Perspective on Invariant and Equivariant Learning","created":"2024-05-28","cited_by_count":27},{"id":"2210.05021","title":"The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective","created":"2022-10-10","cited_by_count":24},{"id":"2410.02698","title":"Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups","created":"2024-10-03","cited_by_count":24},{"id":"2310.06131","title":"Learning Layer-wise Equivariances Automatically using Gradients","created":"2023-10-09","cited_by_count":23},{"id":"2111.05529","title":"Understanding the Generalization Benefit of Model Invariance from a Data Perspective","created":"2021-11-10","cited_by_count":22},{"id":"2202.07552","title":"A Theory of PAC Learnability under Transformation Invariances","created":"2022-02-15","cited_by_count":21},{"id":"1901.11224","title":"Lower Bounds for Smooth Nonconvex Finite-Sum Optimization","created":"2019-01-31","cited_by_count":20},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19},{"id":"2204.03640","title":"Equivariance Discovery by Learned Parameter-Sharing","created":"2022-04-07","cited_by_count":18},{"id":"2209.15031","title":"Automatic Data Augmentation via Invariance-Constrained Learning","created":"2022-09-29","cited_by_count":17},{"id":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16},{"id":"2010.11171","title":"How Data Augmentation affects Optimization for Linear Regression","created":"2020-10-21","cited_by_count":15},{"id":"2302.01915","title":"Sample Complexity of Probability Divergences under Group Symmetry","created":"2023-02-03","cited_by_count":14},{"id":"2205.13900","title":"How Tempering Fixes Data Augmentation in Bayesian Neural Networks","created":"2022-05-27","cited_by_count":13},{"id":"2401.01869","title":"On the hardness of learning under symmetries","created":"2024-01-03","cited_by_count":13},{"id":"2311.02868","title":"Sample Complexity Bounds for Estimating Probability Divergences under Invariances","created":"2023-11-06","cited_by_count":12},{"id":"2210.03773","title":"In What Ways Are Deep Neural Networks Invariant and How Should We Measure This?","created":"2022-10-07","cited_by_count":9},{"id":"2406.13488","title":"Approximately Equivariant Neural Processes","created":"2024-06-19","cited_by_count":8},{"id":"2309.02898","title":"A Unified Framework for Discovering Discrete Symmetries","created":"2023-09-06","cited_by_count":7},{"id":"2512.11855","title":"Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry","created":"2025-12-05","cited_by_count":7},{"id":"2502.19758","title":"Learning with Exact Invariances in Polynomial Time","created":"2025-02-27","cited_by_count":6},{"id":"2606.24418","title":"Data Augmentation: A Fourier Analysis Perspective","created":"2026-06-23","cited_by_count":4},{"id":"2608.08091","title":"Adaptive Symmetry Discovery for Dynamical System Identification","created":"2026-08-08","cited_by_count":3},{"id":"2609.07031","title":"Efficient Learning and Symmetry Discovery under Exact Invariances","created":"2026-09-07","cited_by_count":3}],"next_cursor":null}