| Learning Combinatorial Optimization Algorithms over Graphs H Dai, EB Khalil, Y Zhang, B Dilkina, L Song arXiv preprint arXiv:1704.01665, 2017 | 482 | 2017 |
| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International Conference on Machine Learning, 2702-2711, 2016 | 375 | 2016 |
| Recurrent Marked Temporal Point Processes: Embedding Event History to Vector N Du, H Dai, R Trivedi, U Upadhyay, M Gomez-Rodriguez, L Song Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge …, 2016 | 339 | 2016 |
| Sequential click prediction for sponsored search with recurrent neural networks Y Zhang, H Dai, C Xu, J Feng, T Wang, J Bian, B Wang, TY Liu Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014 | 252 | 2014 |
| Adversarial attack on graph structured data H Dai, H Li, T Tian, X Huang, L Wang, J Zhu, L Song International conference on machine learning, 1115-1124, 2018 | 220 | 2018 |
| Syntax-Directed Variational Autoencoder for Structured Data H Dai, Y Tian, B Dai, S Skiena, L Song arXiv preprint arXiv:1802.08786, 2018 | 176* | 2018 |
| Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs R Trivedi, H Dai, Y Wang, L Song International Conference on Machine Learning, 3462-3471, 2017 | 160 | 2017 |
| A probabilistic model for learning multi-prototype word embeddings F Tian, H Dai, J Bian, B Gao, R Zhang, E Chen, TY Liu Proceedings of COLING 2014, the 25th International Conference on …, 2014 | 151 | 2014 |
| Variational reasoning for question answering with knowledge graph Y Zhang, H Dai, Z Kozareva, AJ Smola, L Song Thirty-Second AAAI Conference on Artificial Intelligence, 2018 | 134 | 2018 |
| Material structure-property linkages using three-dimensional convolutional neural networks A Cecen, H Dai, YC Yabansu, SR Kalidindi, L Song Acta Materialia 146, 76-84, 2018 | 88 | 2018 |
| Deep Coevolutionary Network: Embedding User and Item Features for Recommendation H Dai, Y Wang, R Trivedi, L Song | 79* | 2017 |
| Learning Steady-States of Iterative Algorithms over Graphs H Dai, Z Kozareva, B Dai, A Smola, L Song International Conference on Machine Learning, 1114-1122, 2018 | 71 | 2018 |
| M-Statistic for Kernel Change-Point Detection S Li, Y Xie, H Dai, L Song Advances in Neural Information Processing Systems, 3348-3356, 2015 | 59* | 2015 |
| Provable Bayesian inference via particle mirror descent B Dai, N He, H Dai, L Song Artificial Intelligence and Statistics, 985-994, 2016 | 57* | 2016 |
| Learning loop invariants for program verification X Si, H Dai, M Raghothaman, M Naik, L Song Advances in Neural Information Processing Systems 31, 7751-7762, 2018 | 54 | 2018 |
| CompILE: Compositional Imitation Learning and Execution T Kipf, Y Li, H Dai, V Zambaldi, A Sanchez-Gonzalez, E Grefenstette, ... International Conference on Machine Learning, 3418-3428, 2019 | 33* | 2019 |
| Hoppity: Learning Graph Transformations to Detect and Fix Bugs in Programs E Dinella, H Dai, Z Li, M Naik, L Song, K Wang Proceedings of the International Conference on Learning Representations …, 2020 | 32 | 2020 |
| Recurrent Hidden Semi-Markov Model H Dai, B Dai, YM Zhang, S Li, L Song | 24 | 2016 |
| Retrosynthesis prediction with conditional graph logic network H Dai, C Li, C Coley, B Dai, L Song Advances in Neural Information Processing Systems, 8872-8882, 2019 | 23 | 2019 |
| Sequence2Vec: A novel embedding approach for modeling transcription factor binding affinity landscape H Dai, R Umarov, H Kuwahara, Y Li, L Song, X Gao Bioinformatics, btx480, 2017 | 21 | 2017 |
Le SongGeorgia Institute of TechnologyVerified email at cc.gatech.edu
