{"list":"other","total":80,"rows":[{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"id":"1712.09913","title":"Visualizing the Loss Landscape of Neural Nets","created":"2017-12-28","cited_by_count":1130},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"1711.02257","title":"GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks","created":"2017-11-07","cited_by_count":834},{"id":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"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":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"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":"1911.08251","title":"General $E(2)$-Equivariant Steerable CNNs","created":"2019-11-19","cited_by_count":446},{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441},{"id":"1712.04621","title":"The Effectiveness of Data Augmentation in Image Classification using Deep Learning","created":"2017-12-13","cited_by_count":370},{"id":"1902.04615","title":"Gauge Equivariant Convolutional Networks and the Icosahedral CNN","created":"2019-02-11","cited_by_count":267},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228},{"id":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204},{"id":"1711.07289","title":"Learning Steerable Filters for Rotation Equivariant CNNs","created":"2017-11-20","cited_by_count":194},{"id":"2104.09459","title":"A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups","created":"2021-04-19","cited_by_count":187},{"id":"2111.05464","title":"Are Transformers More Robust Than CNNs?","created":"2021-11-10","cited_by_count":187},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"1906.11172","title":"Learning Data Augmentation Strategies for Object Detection","created":"2019-06-26","cited_by_count":139},{"id":"2206.00133","title":"Pre-training via Denoising for Molecular Property Prediction","created":"2022-05-31","cited_by_count":124},{"id":"2211.06489","title":"Equivariance with Learned Canonicalization Functions","created":"2022-11-11","cited_by_count":109},{"id":"2201.11969","title":"Approximately Equivariant Networks for Imperfectly Symmetric Dynamics","created":"2022-01-28","cited_by_count":97},{"id":"2205.01491","title":"A Comprehensive Survey of Image Augmentation Techniques for Deep Learning","created":"2022-05-03","cited_by_count":97},{"id":"1801.02929","title":"Data Augmentation by Pairing Samples for Images Classification","created":"2018-01-09","cited_by_count":96},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90},{"id":"2103.14147","title":"Equivariant Point Network for 3D Point Cloud Analysis","created":"2021-03-25","cited_by_count":81},{"id":"2005.00178","title":"On the Benefits of Invariance in Neural Networks","created":"2020-05-01","cited_by_count":79},{"id":"2112.01388","title":"Residual Pathway Priors for Soft Equivariance Constraints","created":"2021-12-02","cited_by_count":75},{"id":"1902.07296","title":"Augmentation for small object detection","created":"2019-02-19","cited_by_count":73},{"id":"2305.05577","title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling","created":"2023-04-28","cited_by_count":73},{"id":"2203.06442","title":"Equivariant Graph Mechanics Networks with Constraints","created":"2022-03-12","cited_by_count":68},{"id":"2103.01436","title":"ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations","created":"2021-03-02","cited_by_count":58},{"id":"2311.17932","title":"Swallowing the Bitter Pill: Simplified Scalable Conformer Generation","created":"2023-11-27","cited_by_count":56},{"id":"2210.02984","title":"The Lie Derivative for Measuring Learned Equivariance","created":"2022-10-06","cited_by_count":54},{"id":"2305.18415","title":"Geometric Algebra Transformer","created":"2023-05-28","cited_by_count":48},{"id":"2305.17592","title":"Approximation-Generalization Trade-offs under (Approximate) Group Equivariance","created":"2023-05-27","cited_by_count":46},{"id":"2110.10211","title":"Learning Partial Equivariances from Data","created":"2021-10-19","cited_by_count":45},{"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":"2206.13508","title":"Data Augmentation techniques in time series domain: A survey and taxonomy","created":"2022-06-25","cited_by_count":41},{"id":"2310.01647","title":"Equivariant Adaptation of Large Pretrained Models","created":"2023-10-02","cited_by_count":41},{"id":"2401.11037","title":"Equivariant Graph Neural Operator for Modeling 3D Dynamics","created":"2024-01-19","cited_by_count":38},{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36},{"id":"2310.14197","title":"Diffusion-based Data Augmentation for Nuclei Image Segmentation","created":"2023-10-22","cited_by_count":31},{"id":"2104.04282","title":"Direct Differentiable Augmentation Search","created":"2021-04-09","cited_by_count":28},{"id":"2405.18378","title":"A Canonicalization Perspective on Invariant and Equivariant Learning","created":"2024-05-28","cited_by_count":27},{"id":"2306.02866","title":"Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance","created":"2023-06-05","cited_by_count":26},{"id":"2312.09016","title":"Symmetry Breaking and Equivariant Neural Networks","created":"2023-12-14","cited_by_count":25},{"id":"2202.03990","title":"Equivariance versus Augmentation for Spherical Images","created":"2022-02-08","cited_by_count":24},{"id":"2202.10643","title":"Equivariant Graph Hierarchy-Based Neural Networks","created":"2022-02-22","cited_by_count":24},{"id":"2310.06131","title":"Learning Layer-wise Equivariances Automatically using Gradients","created":"2023-10-09","cited_by_count":23},{"id":"2408.13242","title":"Improving Equivariant Model Training via Constraint Relaxation","created":"2024-08-23","cited_by_count":23},{"id":"2210.06475","title":"Equi-Tuning: Group Equivariant Fine-Tuning of Pretrained Models","created":"2022-10-13","cited_by_count":22},{"id":"2204.13630","title":"Rotationally Equivariant 3D Object Detection","created":"2022-04-28","cited_by_count":21},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19},{"id":"1906.00358","title":"Data Augmentation for Object Detection via Progressive and Selective Instance-Switching","created":"2019-06-02","cited_by_count":18},{"id":"2204.03640","title":"Equivariance Discovery by Learned Parameter-Sharing","created":"2022-04-07","cited_by_count":18},{"id":"2410.11443","title":"Are High-Degree Representations Really Unnecessary in Equivariant Graph Neural Networks?","created":"2024-10-15","cited_by_count":18},{"id":"2306.00356","title":"Regularizing Towards Soft Equivariance Under Mixed Symmetries","created":"2023-06-01","cited_by_count":16},{"id":"2310.08429","title":"Revisiting Data Augmentation for Rotational Invariance in Convolutional Neural Networks","created":"2023-10-12","cited_by_count":16},{"id":"2308.03904","title":"On genuine invariance learning without weight-tying","created":"2023-08-07","cited_by_count":14},{"id":"2407.11086","title":"Pre-training with Fractional Denoising to Enhance Molecular Property Prediction","created":"2024-07-14","cited_by_count":14},{"id":"2111.08190","title":"Learning Augmentation Distributions using Transformed Risk Minimization","created":"2021-11-16","cited_by_count":13},{"id":"2409.00547","title":"Data Augmentation for Image Classification using Generative AI","created":"2024-08-31","cited_by_count":11},{"id":"2310.13164","title":"Almost Equivariance via Lie Algebra Convolutions","created":"2023-10-19","cited_by_count":10},{"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":"2406.03946","title":"A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs","created":"2024-06-06","cited_by_count":8},{"id":"2405.14089","title":"Improved Canonicalization for Model Agnostic Equivariance","created":"2024-05-23","cited_by_count":7},{"id":"2405.17541","title":"Approximately-symmetric neural networks for quantum spin liquids","created":"2024-05-27","cited_by_count":5},{"id":"2409.01793","title":"Task Weighting through Gradient Projection for Multitask Learning","created":"2024-09-03","cited_by_count":4},{"id":"2109.08218","title":"SLAW: Scaled Loss Approximate Weighting for Efficient Multi-Task Learning","created":"2021-09-16","cited_by_count":3},{"id":"2111.00487","title":"Smart(Sampling)Augment: Optimal and Efficient Data Augmentation for Semantic Segmentation","created":"2021-10-31","cited_by_count":3},{"id":"2306.06934","title":"Scale-Rotation-Equivariant Lie Group Convolution Neural Networks (Lie Group-CNNs)","created":"2023-06-12","cited_by_count":2},{"id":"2406.16295","title":"Relaxing Continuous Constraints of Equivariant Graph Neural Networks for Physical Dynamics Learning","created":"2024-06-24","cited_by_count":1},{"id":"2407.04325","title":"Understanding the Role of Invariance in Transfer Learning","created":"2024-07-05","cited_by_count":1}],"next_cursor":null}