{"list":"other","total":123,"rows":[{"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":"1810.00826","title":"How Powerful are Graph Neural Networks?","created":"2018-10-01","cited_by_count":4393},{"id":"1406.1078","title":"Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation","created":"2014-06-03","cited_by_count":4057},{"id":"1312.6114","title":"Auto-Encoding Variational Bayes","created":"2013-12-20","cited_by_count":4053},{"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":"1505.05770","title":"Variational Inference with Normalizing Flows","created":"2015-05-21","cited_by_count":2170},{"id":"1606.09375","title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering","created":"2016-06-30","cited_by_count":2089},{"id":"1605.08803","title":"Density estimation using Real NVP","created":"2016-05-27","cited_by_count":2042},{"id":"1703.06103","title":"Modeling Relational Data with Graph Convolutional Networks","created":"2017-03-17","cited_by_count":1694},{"id":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"1807.03039","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","created":"2018-07-09","cited_by_count":1545},{"id":"1912.02762","title":"Normalizing Flows for Probabilistic Modeling and Inference","created":"2019-12-05","cited_by_count":1323},{"id":"1911.02685","title":"A Comprehensive Survey on Transfer Learning","created":"2019-11-07","cited_by_count":1160},{"id":"1410.8516","title":"NICE: Non-linear Independent Components Estimation","created":"2014-10-30","cited_by_count":1058},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"1906.02691","title":"An Introduction to Variational Autoencoders","created":"2019-06-06","cited_by_count":1000},{"id":"1905.12265","title":"Strategies for Pre-training Graph Neural Networks","created":"2019-05-29","cited_by_count":971},{"id":"1610.02415","title":"Automatic chemical design using a data-driven continuous representation of molecules","created":"2016-10-07","cited_by_count":765},{"id":"1509.09292","title":"Convolutional Networks on Graphs for Learning Molecular Fingerprints","created":"2015-09-30","cited_by_count":754},{"id":"1807.02811","title":"A Tutorial on Bayesian Optimization","created":"2018-07-08","cited_by_count":744},{"id":"1705.07057","title":"Masked Autoregressive Flow for Density Estimation","created":"2017-05-19","cited_by_count":684},{"id":"1802.04364","title":"Junction Tree Variational Autoencoder for Molecular Graph Generation","created":"2018-02-12","cited_by_count":652},{"id":"1704.04760","title":"In-Datacenter Performance Analysis of a Tensor Processing Unit","created":"2017-04-16","cited_by_count":618},{"id":"1511.06349","title":"Generating Sentences from a Continuous Space","created":"2015-11-19","cited_by_count":612},{"id":"1308.0850","title":"Generating Sequences With Recurrent Neural Networks","created":"2013-08-04","cited_by_count":554},{"id":"1708.05866","title":"A Brief Survey of Deep Reinforcement Learning","created":"2017-08-19","cited_by_count":483},{"id":"1802.08773","title":"GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models","created":"2018-02-24","cited_by_count":413},{"id":"1606.05908","title":"Tutorial on Variational Autoencoders","created":"2016-06-19","cited_by_count":409},{"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":"1904.01561","title":"Analyzing Learned Molecular Representations for Property Prediction","created":"2019-04-02","cited_by_count":375},{"id":"1711.01558","title":"Wasserstein Auto-Encoders","created":"2017-11-05","cited_by_count":373},{"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":"1811.09621","title":"GuacaMol: Benchmarking Models for De Novo Molecular Design","created":"2018-11-22","cited_by_count":297},{"id":"1703.01925","title":"Grammar Variational Autoencoder","created":"2017-03-06","cited_by_count":285},{"id":"1811.12823","title":"Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models","created":"2018-11-29","cited_by_count":275},{"id":"1704.07555","title":"Molecular De Novo Design through Deep Reinforcement Learning","created":"2017-04-25","cited_by_count":267},{"id":"1906.02735","title":"Residual Flows for Invertible Generative Modeling","created":"2019-06-06","cited_by_count":257},{"id":"1802.00543","title":"Modeling polypharmacy side effects with graph convolutional networks","created":"2018-02-02","cited_by_count":237},{"id":"1803.03324","title":"Learning Deep Generative Models of Graphs","created":"2018-03-08","cited_by_count":229},{"id":"1506.00019","title":"A Critical Review of Recurrent Neural Networks for Sequence Learning","created":"2015-05-29","cited_by_count":224},{"id":"2002.03230","title":"Hierarchical Generation of Molecular Graphs using Structural Motifs","created":"2020-02-08","cited_by_count":208},{"id":"1810.08678","title":"Optimization of Molecules via Deep Reinforcement Learning","created":"2018-10-19","cited_by_count":200},{"id":"1805.09076","title":"Constrained Graph Variational Autoencoders for Molecule Design","created":"2018-05-23","cited_by_count":185},{"id":"2006.10137","title":"MoFlow: An Invertible Flow Model for Generating Molecular Graphs","created":"2020-06-17","cited_by_count":185},{"id":"2102.01189","title":"GraphDF: A Discrete Flow Model for Molecular Graph Generation","created":"2021-02-01","cited_by_count":180},{"id":"1708.04202","title":"Learning to Plan Chemical Syntheses","created":"2017-08-14","cited_by_count":179},{"id":"1404.2188","title":"A Convolutional Neural Network for Modelling Sentences","created":"2014-04-08","cited_by_count":163},{"id":"1711.10907","title":"Deep Reinforcement Learning for De-Novo Drug Design","created":"2017-11-29","cited_by_count":161},{"id":"2103.04931","title":"Monte Carlo Tree Search: A Review of Recent Modifications and Applications","created":"2021-03-08","cited_by_count":161},{"id":"1603.05629","title":"Discriminative Embeddings of Latent Variable Models for Structured Data","created":"2016-03-17","cited_by_count":131},{"id":"2006.09191","title":"Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining","created":"2020-06-16","cited_by_count":123},{"id":"2007.00523","title":"Drug discovery with explainable artificial intelligence","created":"2020-07-01","cited_by_count":118},{"id":"1705.10843","title":"Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models","created":"2017-05-30","cited_by_count":117},{"id":"1905.11600","title":"GraphNVP: An Invertible Flow Model for Generating Molecular Graphs","created":"2019-05-28","cited_by_count":116},{"id":"2006.05132","title":"A Survey on Generative Adversarial Networks: Variants, Applications, and Training","created":"2020-06-09","cited_by_count":97},{"id":"2007.06686","title":"A Systematic Survey on Deep Generative Models for Graph Generation","created":"2020-07-13","cited_by_count":94},{"id":"1611.03199","title":"Low Data Drug Discovery with One-shot Learning","created":"2016-11-10","cited_by_count":91},{"id":"1904.00314","title":"Molecular geometry prediction using a deep generative graph neural network","created":"2019-03-31","cited_by_count":91},{"id":"1812.01070","title":"Learning Multimodal Graph-to-Graph Translation for Molecular Optimization","created":"2018-12-03","cited_by_count":88},{"id":"2003.12725","title":"A Graph to Graphs Framework for Retrosynthesis Prediction","created":"2020-03-28","cited_by_count":86},{"id":"2102.00546","title":"GraphEBM: Molecular Graph Generation with Energy-Based Models","created":"2021-01-31","cited_by_count":85},{"id":"1905.13372","title":"MolecularRNN: Generating realistic molecular graphs with optimized properties","created":"2019-05-31","cited_by_count":82},{"id":"1908.01425","title":"ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations","created":"2019-08-05","cited_by_count":77},{"id":"1510.00726","title":"A Primer on Neural Network Models for Natural Language Processing","created":"2015-10-02","cited_by_count":76},{"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":"2103.03864","title":"Learning to Extend Molecular Scaffolds with Structural Motifs","created":"2021-03-05","cited_by_count":71},{"id":"2002.07007","title":"The Synthesizability of Molecules Proposed by Generative Models","created":"2020-02-17","cited_by_count":68},{"id":"1902.02119","title":"Mol-CycleGAN - a generative model for molecular optimization","created":"2019-02-06","cited_by_count":66},{"id":"1909.11655","title":"Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space","created":"2019-09-25","cited_by_count":66},{"id":"2005.10036","title":"Uncertainty Quantification Using Neural Networks for Molecular Property Prediction","created":"2020-05-20","cited_by_count":66},{"id":"2005.00065","title":"Generative Adversarial Networks (GANs Survey): Challenges, Solutions, and Future Directions","created":"2020-04-30","cited_by_count":65},{"id":"2006.12179","title":"Hierarchical Inter-Message Passing for Learning on Molecular Graphs","created":"2020-06-22","cited_by_count":59},{"id":"1809.02745","title":"Molecular Hypergraph Grammar with its Application to Molecular Optimization","created":"2018-09-08","cited_by_count":56},{"id":"1812.09441","title":"Graph Transformation Policy Network for Chemical Reaction Prediction","created":"2018-12-22","cited_by_count":56},{"id":"2011.12747","title":"Symmetry-Aware Actor-Critic for 3D Molecular Design","created":"2020-11-25","cited_by_count":55},{"id":"2106.03843","title":"Equivariant Graph Neural Networks for 3D Macromolecular Structure","created":"2021-06-07","cited_by_count":53},{"id":"2002.07717","title":"Reinforcement Learning for Molecular Design Guided by Quantum Mechanics","created":"2020-02-18","cited_by_count":51},{"id":"2007.04897","title":"Guiding Deep Molecular Optimization with Genetic Exploration","created":"2020-07-04","cited_by_count":51},{"id":"2011.01921","title":"Optimizing Molecules using Efficient Queries from Property Evaluations","created":"2020-11-03","cited_by_count":50},{"id":"1803.04465","title":"PotentialNet for Molecular Property Prediction","created":"2018-03-12","cited_by_count":46},{"id":"1805.00108","title":"Conditional molecular design with deep generative models","created":"2018-04-30","cited_by_count":44},{"id":"2004.14949","title":"SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks","created":"2020-04-30","cited_by_count":44},{"id":"1709.03741","title":"Learning Graph-Level Representation for Drug Discovery","created":"2017-09-12","cited_by_count":43},{"id":"1903.04388","title":"Deep learning for molecular design - a review of the state of the art","created":"2019-03-11","cited_by_count":38},{"id":"2101.03438","title":"Quantum Generative Models for Small Molecule Drug Discovery","created":"2021-01-09","cited_by_count":37},{"id":"2004.14308","title":"Molecular Design in Synthetically Accessible Chemical Space via Deep Reinforcement Learning","created":"2020-04-29","cited_by_count":32},{"id":"2106.04011","title":"JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design","created":"2021-06-07","cited_by_count":32},{"id":"2106.05386","title":"Artificial Intelligence in Drug Discovery: Applications and Techniques","created":"2021-06-09","cited_by_count":28},{"id":"1912.05910","title":"CORE: Automatic Molecule Optimization Using Copy & Refine Strategy","created":"2019-11-23","cited_by_count":27},{"id":"2003.13755","title":"Autonomous discovery in the chemical sciences part II: Outlook","created":"2020-03-30","cited_by_count":26},{"id":"2012.05716","title":"Utilising Graph Machine Learning within Drug Discovery and Development","created":"2020-12-09","cited_by_count":26},{"id":"1906.03412","title":"A Two-Step Graph Convolutional Decoder for Molecule Generation","created":"2019-06-08","cited_by_count":25},{"id":"2003.13754","title":"Autonomous discovery in the chemical sciences part I: Progress","created":"2020-03-30","cited_by_count":25},{"id":"2002.06053","title":"Exploring Chemical Space using Natural Language Processing Methodologies for Drug Discovery","created":"2020-02-10","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}],"next_cursor":"100"}