{"list":"other","total":112,"rows":[{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"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":"1706.02216","title":"Inductive Representation Learning on Large Graphs","created":"2017-06-07","cited_by_count":6320},{"id":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1312.6203","title":"Spectral Networks and Locally Connected Networks on Graphs","created":"2013-12-21","cited_by_count":1102},{"id":"1801.07606","title":"Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning","created":"2018-01-22","cited_by_count":1102},{"id":"1506.03134","title":"Pointer Networks","created":"2015-06-09","cited_by_count":895},{"id":"1810.02244","title":"Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks","created":"2018-10-04","cited_by_count":881},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"2003.00982","title":"Benchmarking Graph Neural Networks","created":"2020-03-02","cited_by_count":680},{"id":"1803.08475","title":"Attention, Learn to Solve Routing Problems!","created":"2018-03-22","cited_by_count":656},{"id":"1410.5401","title":"Neural Turing Machines","created":"2014-10-20","cited_by_count":644},{"id":"2006.05205","title":"On the Bottleneck of Graph Neural Networks and its Practical Implications","created":"2020-06-09","cited_by_count":631},{"id":"1811.06128","title":"Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon","created":"2018-11-15","cited_by_count":623},{"id":"1704.04760","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","created":"2017-04-16","cited_by_count":618},{"id":"1611.09940","title":"Neural Combinatorial Optimization with Reinforcement Learning","created":"2016-11-29","cited_by_count":608},{"id":"1704.01665","title":"Learning Combinatorial Optimization Algorithms over Graphs","created":"2017-04-05","cited_by_count":561},{"id":"2004.05718","title":"Principal Neighbourhood Aggregation for Graph Nets","created":"2020-04-12","cited_by_count":478},{"id":"1611.08402","title":"Geometric deep learning on graphs and manifolds using mixture model CNNs","created":"2016-11-25","cited_by_count":468},{"id":"1905.11136","title":"Provably Powerful Graph Networks","created":"2019-05-27","cited_by_count":389},{"id":"1706.01427","title":"A simple neural network module for relational reasoning","created":"2017-06-05","cited_by_count":346},{"id":"1711.07553","title":"Residual Gated Graph ConvNets","created":"2017-11-20","cited_by_count":346},{"id":"1812.09902","title":"Invariant and Equivariant Graph Networks","created":"2018-12-24","cited_by_count":346},{"id":"1802.04240","title":"Reinforcement Learning for Solving the Vehicle Routing Problem","created":"2018-02-12","cited_by_count":333},{"id":"1906.01629","title":"Exact Combinatorial Optimization with Graph Convolutional Neural Networks","created":"2019-06-04","cited_by_count":318},{"id":"1906.01227","title":"An Efficient Graph Convolutional Network Technique for the Travelling Salesman Problem","created":"2019-06-04","cited_by_count":269},{"id":"2003.03600","title":"Reinforcement Learning for Combinatorial Optimization: A Survey","created":"2020-03-07","cited_by_count":236},{"id":"1912.02175","title":"Differentiation of Blackbox Combinatorial Solvers","created":"2019-12-04","cited_by_count":226},{"id":"2009.11848","title":"How Neural Networks Extrapolate: From Feedforward to Graph Neural Networks","created":"2020-09-24","cited_by_count":222},{"id":"2002.06157","title":"Generalization and Representational Limits of Graph Neural Networks","created":"2020-02-14","cited_by_count":218},{"id":"1904.12787","title":"Graph Matching Networks for Learning the Similarity of Graph Structured Objects","created":"2019-04-29","cited_by_count":205},{"id":"1907.03199","title":"What graph neural networks cannot learn: depth vs width","created":"2019-07-06","cited_by_count":201},{"id":"1802.03685","title":"Learning a SAT Solver from Single-Bit Supervision","created":"2018-02-11","cited_by_count":200},{"id":"2010.01179","title":"The Surprising Power of Graph Neural Networks with Random Node Initialization","created":"2020-10-02","cited_by_count":198},{"id":"2002.03155","title":"Random Features Strengthen Graph Neural Networks","created":"2020-02-08","cited_by_count":186},{"id":"1810.10659","title":"Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search","created":"2018-10-25","cited_by_count":183},{"id":"2011.08843","title":"Design Space for Graph Neural Networks","created":"2020-11-17","cited_by_count":182},{"id":"2012.13349","title":"Solving Mixed Integer Programs Using Neural Networks","created":"2020-12-23","cited_by_count":182},{"id":"1905.12149","title":"SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver","created":"2019-05-29","cited_by_count":173},{"id":"1905.12560","title":"On the equivalence between graph isomorphism testing and function approximation with GNNs","created":"2019-05-29","cited_by_count":172},{"id":"2002.05969","title":"Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings","created":"2020-02-14","cited_by_count":171},{"id":"1602.02867","title":"Value Iteration Networks","created":"2016-02-09","cited_by_count":160},{"id":"1905.13211","title":"What Can Neural Networks Reason About?","created":"2019-05-30","cited_by_count":159},{"id":"2005.03675","title":"Machine Learning on Graphs: A Model and Comprehensive Taxonomy","created":"2020-05-07","cited_by_count":156},{"id":"1703.01988","title":"Neural Episodic Control","created":"2017-03-06","cited_by_count":151},{"id":"1903.02541","title":"Relational Pooling for Graph Representations","created":"2019-03-06","cited_by_count":150},{"id":"1603.05629","title":"Discriminative Embeddings of Latent Variable Models for Structured Data","created":"2016-03-17","cited_by_count":131},{"id":"2010.08853","title":"From Local Structures to Size Generalization in Graph Neural Networks","created":"2020-10-17","cited_by_count":124},{"id":"1511.08228","title":"Neural GPUs Learn Algorithms","created":"2015-11-25","cited_by_count":121},{"id":"1910.10593","title":"Neural Execution of Graph Algorithms","created":"2019-10-23","cited_by_count":121},{"id":"1806.01445","title":"Embedding Logical Queries on Knowledge Graphs","created":"2018-06-05","cited_by_count":120},{"id":"2006.10643","title":"Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs","created":"2020-06-18","cited_by_count":115},{"id":"1410.4615","title":"Learning to Execute","created":"2014-10-17","cited_by_count":106},{"id":"1511.06279","title":"Neural Programmer-Interpreters","created":"2015-11-19","cited_by_count":101},{"id":"1811.01900","title":"Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs","created":"2018-11-05","cited_by_count":98},{"id":"2006.07054","title":"Learning the Travelling Salesperson Problem Requires Rethinking Generalization","created":"2020-06-12","cited_by_count":98},{"id":"2003.00330","title":"Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective","created":"2020-02-29","cited_by_count":95},{"id":"2006.15212","title":"Hybrid Models for Learning to Branch","created":"2020-06-26","cited_by_count":95},{"id":"2001.09621","title":"Deep Graph Matching Consensus","created":"2020-01-27","cited_by_count":85},{"id":"2010.13943","title":"Interior Point Solving for LP-based prediction+optimisation","created":"2020-10-26","cited_by_count":83},{"id":"1911.04936","title":"Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement Learning","created":"2019-11-12","cited_by_count":78},{"id":"1711.08028","title":"Recurrent Relational Networks","created":"2017-11-21","cited_by_count":72},{"id":"1808.00508","title":"Neural Arithmetic Logic Units","created":"2018-08-01","cited_by_count":70},{"id":"2005.11081","title":"Learning Combinatorial Optimization on Graphs: A Survey with Applications to Networking","created":"2020-05-22","cited_by_count":70},{"id":"1905.10261","title":"Approximation Ratios of Graph Neural Networks for Combinatorial Problems","created":"2019-05-24","cited_by_count":69},{"id":"1806.01822","title":"Relational recurrent neural networks","created":"2018-06-05","cited_by_count":65},{"id":"2006.01610","title":"Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization","created":"2020-06-02","cited_by_count":63},{"id":"1903.04671","title":"Guiding High-Performance SAT Solvers with Unsat-Core Predictions","created":"2019-03-12","cited_by_count":62},{"id":"1809.02721","title":"Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP","created":"2018-09-08","cited_by_count":58},{"id":"2006.09607","title":"Learning What to Defer for Maximum Independent Sets","created":"2020-06-17","cited_by_count":54},{"id":"2006.06380","title":"Pointer Graph Networks","created":"2020-06-11","cited_by_count":53},{"id":"2008.06319","title":"OR-Gym: A Reinforcement Learning Library for Operations Research Problems","created":"2020-08-14","cited_by_count":51},{"id":"1809.04440","title":"Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set Matching","created":"2018-09-10","cited_by_count":46},{"id":"1912.01329","title":"Neural Network Branching for Neural Network Verification","created":"2019-12-03","cited_by_count":45},{"id":"1806.01203","title":"Relational inductive bias for physical construction in humans and machines","created":"2018-06-04","cited_by_count":44},{"id":"1905.13732","title":"End to end learning and optimization on graphs","created":"2019-05-31","cited_by_count":44},{"id":"2011.06069","title":"Ecole: A Gym-like Library for Machine Learning in Combinatorial Optimization Solvers","created":"2020-11-11","cited_by_count":43},{"id":"1903.04598","title":"Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems","created":"2019-03-11","cited_by_count":40},{"id":"2006.03750","title":"Learning to Solve Combinatorial Optimization Problems on Real-World Graphs in Linear Time","created":"2020-06-06","cited_by_count":39},{"id":"2003.12590","title":"word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data","created":"2020-03-27","cited_by_count":38},{"id":"1905.10307","title":"An Explicitly Relational Neural Network Architecture","created":"2019-05-24","cited_by_count":35},{"id":"1511.06392","title":"Neural Random-Access Machines","created":"2015-11-19","cited_by_count":34},{"id":"2002.09398","title":"It's Not What Machines Can Learn, It's What We Cannot Teach","created":"2020-02-21","cited_by_count":29},{"id":"2005.10026","title":"Reinforcement Learning for Variable Selection in a Branch and Bound Algorithm","created":"2020-05-20","cited_by_count":29},{"id":"2001.05016","title":"Neural Arithmetic Units","created":"2020-01-14","cited_by_count":28},{"id":"2005.09561","title":"Normalized Attention Without Probability Cage","created":"2020-05-19","cited_by_count":20},{"id":"2005.11304","title":"Neural Bipartite Matching","created":"2020-05-22","cited_by_count":17},{"id":"2010.13146","title":"XLVIN: eXecuted Latent Value Iteration Nets","created":"2020-10-25","cited_by_count":17},{"id":"1706.02416","title":"Generalized Value Iteration Networks: Life Beyond Lattices","created":"2017-06-08","cited_by_count":16},{"id":"1908.05767","title":"Experimental performance of graph neural networks on random instances of max-cut","created":"2019-08-15","cited_by_count":15},{"id":"1809.03359","title":"Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning","created":"2018-09-10","cited_by_count":13},{"id":"1710.11417","title":"TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning","created":"2017-10-31","cited_by_count":12}],"next_cursor":"100"}