| Gated graph sequence neural networks Y Li, D Tarlow, M Brockschmidt, R Zemel arXiv preprint arXiv:1511.05493, 2015 | 3542 | 2015 |
| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 3112 | 2018 |
| Understanding the Effective Receptive Field in Deep Convolutional Neural Networks W Luo, Y Li, R Urtasun, R Zemel Advances in Neural Information Processing Systems (NIPS), 2016 | 1732 | 2016 |
| Generative moment matching networks Y Li, K Swersky, R Zemel International conference on machine learning, 1718-1727, 2015 | 966 | 2015 |
| Imagination-Augmented Agents for Deep Reinforcement Learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv:1707.06203, 2017 | 647* | 2017 |
| The variational fair autoencoder C Louizos, K Swersky, Y Li, M Welling, R Zemel arXiv preprint arXiv:1511.00830, 2015 | 646 | 2015 |
| Learning deep generative models of graphs Y Li, O Vinyals, C Dyer, R Pascanu, P Battaglia arXiv preprint arXiv:1803.03324, 2018 | 640 | 2018 |
| Scaling language models: Methods, analysis & insights from training gopher JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ... arXiv preprint arXiv:2112.11446, 2021 | 569 | 2021 |
| Competition-level code generation with alphacode Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ... Science 378 (6624), 1092-1097, 2022 | 562* | 2022 |
| Graph matching networks for learning the similarity of graph structured objects Y Li, C Gu, T Dullien, O Vinyals, P Kohli International conference on machine learning, 3835-3845, 2019 | 508 | 2019 |
| Efficient graph generation with graph recurrent attention networks R Liao, Y Li, Y Song, S Wang, W Hamilton, DK Duvenaud, R Urtasun, ... Advances in neural information processing systems 32, 2019 | 284 | 2019 |
| Relational deep reinforcement learning V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ... arXiv preprint arXiv:1806.01830, 2018 | 255 | 2018 |
| Learning the graphical structure of electronic health records with graph convolutional transformer E Choi, Z Xu, Y Li, M Dusenberry, G Flores, E Xue, A Dai Proceedings of the AAAI conference on artificial intelligence 34 (01), 606-613, 2020 | 217* | 2020 |
| Deep reinforcement learning with relational inductive biases V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ... International conference on learning representations, 2018 | 198 | 2018 |
| Solving mixed integer programs using neural networks V Nair, S Bartunov, F Gimeno, I Von Glehn, P Lichocki, I Lobov, ... arXiv preprint arXiv:2012.13349, 2020 | 178 | 2020 |
| Eta prediction with graph neural networks in google maps A Derrow-Pinion, J She, D Wong, O Lange, T Hester, L Perez, ... Proceedings of the 30th ACM International Conference on Information …, 2021 | 165 | 2021 |
| Compositional imitation learning: Explaining and executing one task at a time T Kipf, Y Li, H Dai, V Zambaldi, E Grefenstette, P Kohli, P Battaglia arXiv preprint arXiv:1812.01483, 2018 | 122* | 2018 |
| Learning Model-Based Planning from Scratch R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ... arXiv:1707.06170, 2017 | 117 | 2017 |
| Reinforced genetic algorithm learning for optimizing computation graphs A Paliwal, F Gimeno, V Nair, Y Li, M Lubin, P Kohli, O Vinyals arXiv preprint arXiv:1905.02494, 2019 | 62 | 2019 |
| Scalable deep generative modeling for sparse graphs H Dai, A Nazi, Y Li, B Dai, D Schuurmans International conference on machine learning, 2302-2312, 2020 | 57 | 2020 |
