{"list":"other","total":55,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1605.07146","title":"Wide Residual Networks","created":"2016-05-23","cited_by_count":3070},{"id":"1610.02136","title":"A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks","created":"2016-10-07","cited_by_count":2385},{"id":"1807.03888","title":"A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks","created":"2018-07-10","cited_by_count":1485},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2010.03759","title":"Energy-based Out-of-distribution Detection","created":"2020-10-08","cited_by_count":1299},{"id":"1901.05555","title":"Class-Balanced Loss Based on Effective Number of Samples","created":"2019-01-16","cited_by_count":1245},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"id":"1906.07413","title":"Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss","created":"2019-06-18","cited_by_count":989},{"id":"1801.09414","title":"CosFace: Large Margin Cosine Loss for Deep Face Recognition","created":"2018-01-29","cited_by_count":971},{"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":"1704.08063","title":"SphereFace: Deep Hypersphere Embedding for Face Recognition","created":"2017-04-26","cited_by_count":737},{"id":"2008.08186","title":"Prevalence of Neural Collapse during the terminal phase of deep learning training","created":"2020-08-18","cited_by_count":719},{"id":"1904.05160","title":"Large-Scale Long-Tailed Recognition in an Open World","created":"2019-04-10","cited_by_count":705},{"id":"2003.03123","title":"Directional Message Passing for Molecular Graphs","created":"2020-03-06","cited_by_count":561},{"id":"2110.06207","title":"Open-Set Recognition: a Good Closed-Set Classifier is All You Need?","created":"2021-10-12","cited_by_count":353},{"id":"1612.02295","title":"Large-Margin Softmax Loss for Convolutional Neural Networks","created":"2016-12-07","cited_by_count":296},{"id":"1612.04642","title":"Harmonic Networks: Deep Translation and Rotation Equivariance","created":"2016-12-14","cited_by_count":295},{"id":"1902.04615","title":"Gauge Equivariant Convolutional Networks and the Icosahedral CNN","created":"2019-02-11","cited_by_count":267},{"id":"1906.04015","title":"Cormorant: Covariant Molecular Neural Networks","created":"2019-06-06","cited_by_count":263},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228},{"id":"2105.02375","title":"A Geometric Analysis of Neural Collapse with Unconstrained Features","created":"2021-05-06","cited_by_count":227},{"id":"2106.03348","title":"ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias","created":"2021-06-07","cited_by_count":216},{"id":"2104.00466","title":"Improving Calibration for Long-Tailed Recognition","created":"2021-04-01","cited_by_count":212},{"id":"1909.02487","title":"Ab-Initio Solution of the Many-Electron Schr\\\"odinger Equation with Deep Neural Networks","created":"2019-09-05","cited_by_count":200},{"id":"1810.09202","title":"Graph Convolutional Reinforcement Learning","created":"2018-10-22","cited_by_count":152},{"id":"2006.16908","title":"MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning","created":"2020-06-30","cited_by_count":142},{"id":"1911.12207","title":"Orthogonal Convolutional Neural Networks","created":"2019-11-27","cited_by_count":123},{"id":"1905.11697","title":"Deep Scale-spaces: Equivariance Over Scale","created":"2019-05-28","cited_by_count":114},{"id":"1910.11093","title":"Scale-Equivariant Steerable Networks","created":"2019-10-14","cited_by_count":110},{"id":"1910.00091","title":"Deep Coordination Graphs","created":"2019-09-27","cited_by_count":98},{"id":"1602.02660","title":"Exploiting Cyclic Symmetry in Convolutional Neural Networks","created":"2016-02-08","cited_by_count":91},{"id":"1901.10514","title":"Hyperspherical Prototype Networks","created":"2019-01-29","cited_by_count":86},{"id":"1805.09298","title":"Learning towards Minimum Hyperspherical Energy","created":"2018-05-23","cited_by_count":75},{"id":"2110.15443","title":"Equivariant $Q$ Learning in Spatial Action Spaces","created":"2021-10-28","cited_by_count":62},{"id":"1703.01827","title":"All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation","created":"2017-03-06","cited_by_count":58},{"id":"1711.03189","title":"Deep Hyperspherical Learning","created":"2017-11-08","cited_by_count":55},{"id":"2011.12747","title":"Symmetry-Aware Actor-Critic for 3D Molecular Design","created":"2020-11-25","cited_by_count":55},{"id":"1911.00025","title":"PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning","created":"2019-10-31","cited_by_count":47},{"id":"1804.08071","title":"Decoupled Networks","created":"2018-04-22","cited_by_count":45},{"id":"2103.14021","title":"Orthogonal Projection Loss","created":"2021-03-25","cited_by_count":44},{"id":"1611.01967","title":"Regularizing CNNs with Locally Constrained Decorrelations","created":"2016-11-07","cited_by_count":41},{"id":"2106.14472","title":"Hyperbolic Busemann Learning with Ideal Prototypes","created":"2021-06-28","cited_by_count":41},{"id":"1803.00130","title":"Ring loss: Convex Feature Normalization for Face Recognition","created":"2018-02-28","cited_by_count":39},{"id":"2107.01125","title":"On Measuring and Controlling the Spectral Bias of the Deep Image Prior","created":"2021-07-02","cited_by_count":36},{"id":"1901.11399","title":"Equivariant Transformer Networks","created":"2019-01-25","cited_by_count":35},{"id":"2103.01649","title":"Learning with Hyperspherical Uniformity","created":"2021-03-02","cited_by_count":35},{"id":"1806.05086","title":"Group Equivariant Capsule Networks","created":"2018-06-13","cited_by_count":31},{"id":"1906.04892","title":"Regularizing Neural Networks via Minimizing Hyperspherical Energy","created":"2019-06-12","cited_by_count":25},{"id":"2110.04495","title":"Multi-Agent MDP Homomorphic Networks","created":"2021-10-09","cited_by_count":25},{"id":"2006.06527","title":"MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles","created":"2020-06-06","cited_by_count":15},{"id":"2004.09033","title":"OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer","created":"2020-04-20","cited_by_count":7}],"next_cursor":null}