{"list":"other","total":66,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"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":"1610.01145","title":"Error bounds for approximations with deep ReLU networks","created":"2016-10-03","cited_by_count":574},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"2206.07697","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields","created":"2022-06-15","cited_by_count":474},{"id":"1912.10077","title":"Are Transformers universal approximators of sequence-to-sequence functions?","created":"2019-12-20","cited_by_count":406},{"id":"1904.11486","title":"Making Convolutional Networks Shift-Invariant Again","created":"2019-04-25","cited_by_count":391},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"1412.8690","title":"Breaking the Curse of Dimensionality with Convex Neural Networks","created":"2014-12-30","cited_by_count":331},{"id":"2104.12229","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","created":"2021-04-25","cited_by_count":305},{"id":"1905.04943","title":"Universal Invariant and Equivariant Graph Neural Networks","created":"2019-05-13","cited_by_count":183},{"id":"1709.05289","title":"Optimal approximation of piecewise smooth functions using deep ReLU neural networks","created":"2017-09-15","cited_by_count":179},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"1802.03620","title":"Optimal approximation of continuous functions by very deep ReLU networks","created":"2018-02-10","cited_by_count":166},{"id":"2001.03040","title":"Deep Network Approximation for Smooth Functions","created":"2020-01-09","cited_by_count":164},{"id":"1901.09342","title":"On the Universality of Invariant Networks","created":"2019-01-27","cited_by_count":152},{"id":"1804.10306","title":"Universal approximations of invariant maps by neural networks","created":"2018-04-26","cited_by_count":127},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127},{"id":"2006.04780","title":"Lorentz Group Equivariant Neural Network for Particle Physics","created":"2020-06-08","cited_by_count":121},{"id":"2301.12780","title":"Equivariant Architectures for Learning in Deep Weight Spaces","created":"2023-01-30","cited_by_count":107},{"id":"2201.11969","title":"Approximately Equivariant Networks for Imperfectly Symmetric Dynamics","created":"2022-01-28","cited_by_count":97},{"id":"2103.00502","title":"Optimal Approximation Rate of ReLU Networks in terms of Width and Depth","created":"2021-02-28","cited_by_count":96},{"id":"1902.07896","title":"Error bounds for approximations with deep ReLU neural networks in $W^{s,p}$ norms","created":"2019-02-21","cited_by_count":95},{"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":"2101.12365","title":"Sharp Bounds on the Approximation Rates, Metric Entropy, and $n$-widths of Shallow Neural Networks","created":"2021-01-29","cited_by_count":86},{"id":"1607.07819","title":"Approximation by Combinations of ReLU and Squared ReLU Ridge Functions with $ \\ell^1 $ and $ \\ell^0 $ Controls","created":"2016-07-26","cited_by_count":84},{"id":"2010.02449","title":"On the Universality of Rotation Equivariant Point Cloud Networks","created":"2020-10-06","cited_by_count":80},{"id":"2305.05577","title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling","created":"2023-04-28","cited_by_count":73},{"id":"2107.01959","title":"Universal Approximation of Functions on Sets","created":"2021-07-05","cited_by_count":62},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62},{"id":"1509.07385","title":"Provable approximation properties for deep neural networks","created":"2015-09-24","cited_by_count":60},{"id":"2410.23179","title":"Does equivariance matter at scale?","created":"2024-10-30","cited_by_count":52},{"id":"2211.14400","title":"Optimal Approximation Rates for Deep ReLU Neural Networks on Sobolev and Besov Spaces","created":"2022-11-25","cited_by_count":51},{"id":"2305.17592","title":"Approximation-Generalization Trade-offs under (Approximate) Group Equivariance","created":"2023-05-27","cited_by_count":46},{"id":"1910.02421","title":"On Universal Equivariant Set Networks","created":"2019-10-06","cited_by_count":45},{"id":"2402.16077","title":"Equivariant Frames and the Impossibility of Continuous Canonicalization","created":"2024-02-25","cited_by_count":43},{"id":"1706.03301","title":"Neural networks and rational functions","created":"2017-06-11","cited_by_count":40},{"id":"2110.03187","title":"On the Optimal Memorization Power of ReLU Neural Networks","created":"2021-10-07","cited_by_count":40},{"id":"2305.19302","title":"Smooth, exact rotational symmetrization for deep learning on point clouds","created":"2023-05-30","cited_by_count":39},{"id":"1910.06552","title":"Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces","created":"2019-10-15","cited_by_count":34},{"id":"2307.02301","title":"Sumformer: Universal Approximation for Efficient Transformers","created":"2023-07-05","cited_by_count":30},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22},{"id":"2203.07546","title":"Permutation Invariant Representations with Applications to Graph Deep Learning","created":"2022-03-14","cited_by_count":20},{"id":"2201.07136","title":"Incompleteness of graph neural networks for points clouds in three dimensions","created":"2022-01-18","cited_by_count":19},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19},{"id":"2205.14039","title":"Group-invariant max filtering","created":"2022-05-27","cited_by_count":18},{"id":"2310.08429","title":"Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks","created":"2023-10-12","cited_by_count":16},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11},{"id":"2206.01266","title":"Exponential Separations in Symmetric Neural Networks","created":"2022-06-02","cited_by_count":10},{"id":"2301.13821","title":"Complete Neural Networks for Complete Euclidean Graphs","created":"2023-01-31","cited_by_count":10},{"id":"2402.02484","title":"Weisfeiler Leman for Euclidean Equivariant Machine Learning","created":"2024-02-04","cited_by_count":10},{"id":"2303.12853","title":"Three iterations of $(d-1)$-WL test distinguish non isometric clouds of $d$-dimensional points","created":"2023-03-22","cited_by_count":9},{"id":"2410.09118","title":"FSW-GNN: A Bi-Lipschitz WL-Equivalent Graph Neural Network","created":"2024-10-10","cited_by_count":8},{"id":"2504.02544","title":"Fourier Sliced-Wasserstein Embedding for Multisets and Measures","created":"2025-04-03","cited_by_count":8},{"id":"2407.02025","title":"On the Expressive Power of Sparse Geometric MPNNs","created":"2024-07-02","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":"2212.05104","title":"Max filtering with reflection groups","created":"2022-12-09","cited_by_count":4},{"id":"2502.11467","title":"Approximation of Permutation Invariant Polynomials by Transformers: Efficient Construction in Column-Size","created":"2025-02-17","cited_by_count":3},{"id":"2503.04263","title":"Bi-Lipschitz Ansatz for Anti-Symmetric Functions","created":"2025-03-06","cited_by_count":3},{"id":"2511.11735","title":"Toward bilipshiz geometric models","created":"2025-11-13","cited_by_count":1}],"next_cursor":null}