{"list":"other","total":55,"rows":[{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"2107.03374","title":"Evaluating Large Language Models Trained on Code","created":"2021-07-07","cited_by_count":8320},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"2203.00667","title":"Generative Adversarial Networks","created":"2022-03-01","cited_by_count":5627},{"id":"1412.3555","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","created":"2014-12-11","cited_by_count":2367},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"1701.02426","title":"Scene Graph Generation by Iterative Message Passing","created":"2017-01-10","cited_by_count":462},{"id":"1802.08773","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","created":"2018-02-24","cited_by_count":413},{"id":"1805.11973","title":"MolGAN: An implicit generative model for small molecular graphs","created":"2018-05-30","cited_by_count":393},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"1806.02473","title":"Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation","created":"2018-06-07","cited_by_count":364},{"id":"2001.09382","title":"GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation","created":"2020-01-26","cited_by_count":305},{"id":"1811.12823","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","created":"2018-11-29","cited_by_count":275},{"id":"1511.06391","title":"Order Matters: Sequence to sequence for sets","created":"2015-11-19","cited_by_count":237},{"id":"1910.00760","title":"Efficient Graph Generation with Graph Recurrent Attention Networks","created":"2019-10-02","cited_by_count":234},{"id":"1711.00740","title":"Learning to Represent Programs with Graphs","created":"2017-11-01","cited_by_count":220},{"id":"1803.09518","title":"Fr\\'echet ChemNet Distance: A metric for generative models for molecules in drug discovery","created":"2018-03-26","cited_by_count":194},{"id":"2006.10137","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","created":"2020-06-17","cited_by_count":185},{"id":"1803.07294","title":"GaAN: Gated Attention Networks for Learning on Large and Spatiotemporal Graphs","created":"2018-03-20","cited_by_count":165},{"id":"2105.09016","title":"E(n) Equivariant Normalizing Flows","created":"2021-05-19","cited_by_count":165},{"id":"1905.11600","title":"GraphNVP: An Invertible Flow Model for Generating Molecular Graphs","created":"2019-05-28","cited_by_count":116},{"id":"1802.08786","title":"Syntax-Directed Variational Autoencoder for Structured Data","created":"2018-02-24","cited_by_count":103},{"id":"1811.01900","title":"Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs","created":"2018-11-05","cited_by_count":98},{"id":"2007.06686","title":"A Systematic Survey on Deep Generative Models for Graph Generation","created":"2020-07-13","cited_by_count":94},{"id":"2109.04824","title":"Inverse design of 3d molecular structures with conditional generative neural networks","created":"2021-09-10","cited_by_count":93},{"id":"1905.13372","title":"MolecularRNN: Generating realistic molecular graphs with optimized properties","created":"2019-05-31","cited_by_count":82},{"id":"1809.02630","title":"Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders","created":"2018-09-07","cited_by_count":74},{"id":"1806.05805","title":"Molecular generative model based on conditional variational autoencoder for de novo molecular design","created":"2018-06-15","cited_by_count":73},{"id":"2203.06714","title":"A Survey on Deep Graph Generation: Methods and Applications","created":"2022-03-13","cited_by_count":69},{"id":"2112.03041","title":"Keeping it Simple: Language Models can learn Complex Molecular Distributions","created":"2021-12-06","cited_by_count":68},{"id":"2001.08184","title":"GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation","created":"2020-01-22","cited_by_count":66},{"id":"1805.08490","title":"Generative Code Modeling with Graphs","created":"2018-05-22","cited_by_count":60},{"id":"2011.12747","title":"Symmetry-Aware Actor-Critic for 3D Molecular Design","created":"2020-11-25","cited_by_count":55},{"id":"2002.07717","title":"Reinforcement Learning for Molecular Design Guided by Quantum Mechanics","created":"2020-02-18","cited_by_count":51},{"id":"1802.05451","title":"Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction","created":"2018-02-15","cited_by_count":44},{"id":"1805.00108","title":"Conditional molecular design with deep generative models","created":"2018-04-30","cited_by_count":44},{"id":"2106.06189","title":"Order Matters: Probabilistic Modeling of Node Sequence for Graph Generation","created":"2021-06-11","cited_by_count":33},{"id":"1711.07839","title":"Application of generative autoencoder in de novo molecular design","created":"2017-11-21","cited_by_count":32},{"id":"1811.09766","title":"DEFactor: Differentiable Edge Factorization-based Probabilistic Graph Generation","created":"2018-11-24","cited_by_count":24},{"id":"2002.12826","title":"A Deep Generative Model for Fragment-Based Molecule Generation","created":"2020-02-28","cited_by_count":24},{"id":"2006.05385","title":"Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement","created":"2020-06-09","cited_by_count":24},{"id":"1909.13521","title":"Graph Residual Flow for Molecular Graph Generation","created":"2019-09-30","cited_by_count":23},{"id":"2002.00102","title":"Edge-based sequential graph generation with recurrent neural networks","created":"2020-01-31","cited_by_count":16},{"id":"2002.07087","title":"Graph Deconvolutional Generation","created":"2020-02-14","cited_by_count":16},{"id":"2203.00412","title":"Interpretable Molecular Graph Generation via Monotonic Constraints","created":"2022-02-28","cited_by_count":15},{"id":"2010.13055","title":"Regularizing Towards Permutation Invariance in Recurrent Models","created":"2020-10-25","cited_by_count":13},{"id":"1906.03220","title":"Labeled Graph Generative Adversarial Networks","created":"2019-06-07","cited_by_count":12},{"id":"2203.00411","title":"Disentangled Spatiotemporal Graph Generative Models","created":"2022-02-28","cited_by_count":12},{"id":"2305.15562","title":"Let There Be Order: Rethinking Ordering in Autoregressive Graph Generation","created":"2023-05-24","cited_by_count":7},{"id":"1808.07624","title":"Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model","created":"2018-08-23","cited_by_count":6},{"id":"2306.11412","title":"Size Matters: Large Graph Generation with HiGGs","created":"2023-06-20","cited_by_count":3}],"next_cursor":null}