| Learning Combinatorial Optimization Algorithms over Graphs H Dai, EB Khalil, Y Zhang, B Dilkina, L Song Advances in Neural Information Processing Systems, 6348-6358, 2017 | 1340 | 2017 |
| Discriminative embeddings of latent variable models for structured data H Dai, B Dai, L Song International Conference on Machine Learning, 2702-2711, 2016 | 710 | 2016 |
| 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 | 659 | 2018 |
| 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 | 649 | 2016 |
| 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 | 429 | 2017 |
| 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 | 407 | 2018 |
| 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 | 381 | 2014 |
| Syntax-Directed Variational Autoencoder for Structured Data H Dai, Y Tian, B Dai, S Skiena, L Song Proceedings of the International Conference on Learning Representations …, 2018 | 363 | 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 | 258 | 2018 |
| 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 | 194 | 2018 |
| 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 | 186 | 2020 |
| 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 | 180 | 2014 |
| Deep Coevolutionary Network: Embedding User and Item Features for Recommendation H Dai, Y Wang, R Trivedi, L Song | 157* | 2017 |
| 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 | 137 | 2019 |
| 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 | 125 | 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 | 115* | 2019 |
| M-Statistic for Kernel Change-Point Detection S Li, Y Xie, H Dai, L Song Advances in Neural Information Processing Systems, 3348-3356, 2015 | 110 | 2015 |
| Retro*: Learning Retrosynthetic Planning with Neural Guided A* Search B Chen, C Li, H Dai, L Song International Conference on Machine Learning, 1608-1616, 2020 | 79 | 2020 |
| Differentiable Top-k with Optimal Transport Y Xie, H Dai, M Chen, B Dai, T Zhao, H Zha, W Wei, T Pfister Advances in Neural Information Processing Systems 33, 2020 | 78 | 2020 |
| Provable Bayesian inference via particle mirror descent B Dai, N He, H Dai, L Song Artificial Intelligence and Statistics, 985-994, 2016 | 63 | 2016 |
Le SongBiomap, Mohamed bin Zayed University of Artificial IntelligenceVerified email at biomap.com
