{"list":"other","total":100,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"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":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1502.03167","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","created":"2015-02-11","cited_by_count":7005},{"id":"1409.4842","title":"Going Deeper with Convolutions","created":"2014-09-17","cited_by_count":4241},{"id":"1406.1078","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","created":"2014-06-03","cited_by_count":4057},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"id":"1312.5602","title":"Playing Atari with Deep Reinforcement Learning","created":"2013-12-19","cited_by_count":3274},{"id":"1703.05175","title":"Prototypical Networks for Few-shot Learning","created":"2017-03-15","cited_by_count":3015},{"id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","created":"2018-02-09","cited_by_count":2898},{"id":"1606.04080","title":"Matching Networks for One Shot Learning","created":"2016-06-13","cited_by_count":2255},{"id":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1508.05326","title":"A large annotated corpus for learning natural language inference","created":"2015-08-21","cited_by_count":1847},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"1801.07455","title":"Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition","created":"2018-01-23","cited_by_count":1209},{"id":"1607.08022","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","created":"2016-07-27","cited_by_count":1198},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1811.05868","title":"Pitfalls of Graph Neural Network Evaluation","created":"2018-11-14","cited_by_count":1007},{"id":"1412.6575","title":"Embedding Entities and Relations for Learning and Inference in Knowledge Bases","created":"2014-12-20","cited_by_count":932},{"id":"1607.01759","title":"Bag of Tricks for Efficient Text Classification","created":"2016-07-06","cited_by_count":876},{"id":"1904.09237","title":"On the Convergence of Adam and Beyond","created":"2019-04-19","cited_by_count":841},{"id":"1705.08039","title":"Poincar\\'e Embeddings for Learning Hierarchical Representations","created":"2017-05-22","cited_by_count":684},{"id":"1303.5778","title":"Speech Recognition with Deep Recurrent Neural Networks","created":"2013-03-22","cited_by_count":669},{"id":"1505.00853","title":"Empirical Evaluation of Rectified Activations in Convolutional Network","created":"2015-05-05","cited_by_count":519},{"id":"1801.10130","title":"Spherical CNNs","created":"2018-01-30","cited_by_count":456},{"id":"1805.09112","title":"Hyperbolic Neural Networks","created":"2018-05-23","cited_by_count":402},{"id":"1910.12933","title":"Hyperbolic Graph Convolutional Neural Networks","created":"2019-10-28","cited_by_count":402},{"id":"1711.01558","title":"Wasserstein Auto-Encoders","created":"2017-11-05","cited_by_count":373},{"id":"1609.03552","title":"Generative Visual Manipulation on the Natural Image Manifold","created":"2016-09-12","cited_by_count":348},{"id":"1806.03417","title":"Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry","created":"2018-06-09","cited_by_count":265},{"id":"1705.07120","title":"VAE with a VampPrior","created":"2017-05-19","cited_by_count":248},{"id":"1711.06721","title":"Learning SO(3) Equivariant Representations with Spherical CNNs","created":"2017-11-17","cited_by_count":248},{"id":"1506.05163","title":"Deep Convolutional Networks on Graph-Structured Data","created":"2015-06-16","cited_by_count":246},{"id":"1910.12892","title":"Hyperbolic Graph Neural Networks","created":"2019-10-28","cited_by_count":244},{"id":"1904.02239","title":"Hyperbolic Image Embeddings","created":"2019-04-03","cited_by_count":221},{"id":"1804.03329","title":"Representation Tradeoffs for Hyperbolic Embeddings","created":"2018-04-10","cited_by_count":206},{"id":"1810.00760","title":"Riemannian Adaptive Optimization Methods","created":"2018-10-01","cited_by_count":194},{"id":"1804.00891","title":"Hyperspherical Variational Auto-Encoders","created":"2018-04-03","cited_by_count":189},{"id":"1610.02984","title":"Person Re-identification: Past, Present and Future","created":"2016-10-10","cited_by_count":181},{"id":"1804.01882","title":"Hyperbolic Entailment Cones for Learning Hierarchical Embeddings","created":"2018-04-03","cited_by_count":162},{"id":"1810.06546","title":"Poincar\\'e GloVe: Hyperbolic Word Embeddings","created":"2018-10-15","cited_by_count":156},{"id":"2006.10605","title":"Riemannian Continuous Normalizing Flows","created":"2020-06-18","cited_by_count":145},{"id":"1810.00825","title":"Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks","created":"2018-10-01","cited_by_count":132},{"id":"1905.09791","title":"Multi-relational Poincar\\'e Graph Embeddings","created":"2019-05-23","cited_by_count":126},{"id":"1805.09786","title":"Hyperbolic Attention Networks","created":"2018-05-24","cited_by_count":122},{"id":"2006.04780","title":"Lorentz Group Equivariant Neural Network for Particle Physics","created":"2020-06-08","cited_by_count":121},{"id":"2003.13913","title":"Flows for simultaneous manifold learning and density estimation","created":"2020-03-31","cited_by_count":118},{"id":"2002.02428","title":"Normalizing Flows on Tori and Spheres","created":"2020-02-06","cited_by_count":117},{"id":"2005.02819","title":"Geoopt: Riemannian Optimization in PyTorch","created":"2020-05-06","cited_by_count":114},{"id":"1511.06361","title":"Order-Embeddings of Images and Language","created":"2015-11-19","cited_by_count":113},{"id":"1607.05447","title":"On Differentiating Parameterized Argmin and Argmax Problems with Application to Bi-level Optimization","created":"2016-07-19","cited_by_count":112},{"id":"1901.06033","title":"Continuous Hierarchical Representations with Poincar\\'e Variational Auto-Encoders","created":"2019-01-17","cited_by_count":112},{"id":"1911.05076","title":"Constant Curvature Graph Convolutional Networks","created":"2019-11-12","cited_by_count":108},{"id":"1602.06053","title":"First-order Methods for Geodesically Convex Optimization","created":"2016-02-19","cited_by_count":93},{"id":"1911.08411","title":"Mixed-curvature Variational Autoencoders","created":"2019-11-19","cited_by_count":86},{"id":"1806.04313","title":"Embedding Text in Hyperbolic Spaces","created":"2018-06-12","cited_by_count":76},{"id":"1911.04131","title":"Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching","created":"2019-11-11","cited_by_count":70},{"id":"1912.03046","title":"Hyperbolic Graph Attention Network","created":"2019-12-06","cited_by_count":70},{"id":"1611.02304","title":"Normalizing Flows on Riemannian Manifolds","created":"2016-11-07","cited_by_count":68},{"id":"2012.15000","title":"DeepSphere: a graph-based spherical CNN","created":"2020-12-30","cited_by_count":66},{"id":"2010.00402","title":"From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering","created":"2020-10-01","cited_by_count":65},{"id":"1510.05043","title":"A cost function for similarity-based hierarchical clustering","created":"2015-10-16","cited_by_count":63},{"id":"1810.12186","title":"DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications","created":"2018-10-29","cited_by_count":62},{"id":"1705.10359","title":"Neural Embeddings of Graphs in Hyperbolic Space","created":"2017-05-29","cited_by_count":58},{"id":"2010.12135","title":"Graph Geometry Interaction Learning","created":"2020-10-23","cited_by_count":55},{"id":"2002.06336","title":"Latent Variable Modelling with Hyperbolic Normalizing Flows","created":"2020-02-15","cited_by_count":49},{"id":"1809.01703","title":"HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems","created":"2018-09-05","cited_by_count":47},{"id":"1607.07195","title":"Higher-Order Factorization Machines","created":"2016-07-25","cited_by_count":45},{"id":"1806.00437","title":"Large-Margin Classification in Hyperbolic Space","created":"2018-06-01","cited_by_count":41},{"id":"2005.03847","title":"Tree! I am no Tree! I am a Low Dimensional Hyperbolic Embedding","created":"2020-05-08","cited_by_count":36},{"id":"1606.06737","title":"Criticality in Formal Languages and Statistical Physics","created":"2016-06-21","cited_by_count":35},{"id":"2010.16143","title":"HyperText: Endowing FastText with Hyperbolic Geometry","created":"2020-10-30","cited_by_count":28},{"id":"1608.02117","title":"HyperLex: A Large-Scale Evaluation of Graded Lexical Entailment","created":"2016-08-06","cited_by_count":26},{"id":"1707.07847","title":"Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering","created":"2017-07-25","cited_by_count":23},{"id":"1906.02002","title":"Every child should have parents: a taxonomy refinement algorithm based on hyperbolic term embeddings","created":"2019-06-05","cited_by_count":23},{"id":"1802.03761","title":"On the Latent Space of Wasserstein Auto-Encoders","created":"2018-02-11","cited_by_count":22},{"id":"1602.06522","title":"Machine learning meets network science: dimensionality reduction for fast and efficient embedding of networks in the hyperbolic space","created":"2016-02-21","cited_by_count":21},{"id":"2004.05465","title":"Robust Large-Margin Learning in Hyperbolic Space","created":"2020-04-11","cited_by_count":21},{"id":"1908.04895","title":"HyperKG: Hyperbolic Knowledge Graph Embeddings for Knowledge Base Completion","created":"2019-08-14","cited_by_count":20},{"id":"1902.08648","title":"Scalable Hyperbolic Recommender Systems","created":"2019-02-22","cited_by_count":19},{"id":"2007.00211","title":"Ultrahyperbolic Representation Learning","created":"2020-07-01","cited_by_count":17},{"id":"2005.00054","title":"APo-VAE: Text Generation in Hyperbolic Space","created":"2020-04-30","cited_by_count":14},{"id":"2007.15678","title":"Mix Dimension in Poincar\\'{e} Geometry for 3D Skeleton-based Action Recognition","created":"2020-07-30","cited_by_count":14},{"id":"1812.04314","title":"Adversarial Autoencoders with Constant-Curvature Latent Manifolds","created":"2018-12-11","cited_by_count":12},{"id":"2006.00986","title":"Semi-Supervised Hierarchical Drug Embedding in Hyperbolic Space","created":"2020-06-01","cited_by_count":9},{"id":"1911.09821","title":"Learning Feature Interactions with Lorentzian Factorization Machine","created":"2019-11-22","cited_by_count":8},{"id":"1901.01427","title":"Poincar\\'e Wasserstein Autoencoder","created":"2019-01-05","cited_by_count":7},{"id":"2007.07698","title":"Are Hyperbolic Representations in Graphs Created Equal?","created":"2020-07-15","cited_by_count":4},{"id":"1502.01271","title":"INRIASAC: Simple Hypernym Extraction Methods","created":"2015-02-04","cited_by_count":3},{"id":"1812.10408","title":"Hyperbolic Deep Learning for Chinese Natural Language Understanding","created":"2018-12-11","cited_by_count":3},{"id":"1608.04200","title":"Cross Euclidean-to-Riemannian Metric Learning with Application to Face Recognition from Video","created":"2016-08-15","cited_by_count":2}],"next_cursor":null}