| Gated graph sequence neural networks Y Li, D Tarlow, M Brockschmidt, R Zemel arXiv preprint arXiv:1511.05493, 2015 | 2318 | 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 | 1912 | 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 | 1018 | 2016 |
| Generative moment matching networks Y Li, K Swersky, R Zemel International conference on machine learning, 1718-1727, 2015 | 757 | 2015 |
| Imagination-Augmented Agents for Deep Reinforcement Learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv:1707.06203, 2017 | 503* | 2017 |
| The variational fair autoencoder C Louizos, K Swersky, Y Li, M Welling, R Zemel arXiv preprint arXiv:1511.00830, 2015 | 475 | 2015 |
| Learning deep generative models of graphs Y Li, O Vinyals, C Dyer, R Pascanu, P Battaglia arXiv preprint arXiv:1803.03324, 2018 | 439 | 2018 |
| 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 | 263 | 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 | 192 | 2018 |
| 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 | 151 | 2019 |
| 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 | 126 | 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 | 100 | 2017 |
| 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 | 67* | 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 | 54 | 2021 |
| 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 | 53 | 2020 |
| Exploring compositional high order pattern potentials for structured output learning Y Li, D Tarlow, R Zemel Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2013 | 52 | 2013 |
| Mean Field Networks Y Li, R Zemel ICML workshop on Learning Tractable Probabilistic Models, 2014 | 34 | 2014 |
| Graph convolutional transformer: Learning the graphical structure of electronic health records E Choi, Z Xu, Y Li, MW Dusenberry, G Flores, Y Xue, AM Dai arXiv preprint arXiv:1906.04716, 2019 | 32 | 2019 |
| Learning unbiased features Y Li, K Swersky, R Zemel NIPS workshop on Transfer and Multi-Task Learnnig, 2014 | 28 | 2014 |
| Celebrity Recommendation with Collaborative Social Topic Regression. X Ding, X Jin, Y Li, L Li IJCAI, 2612-2618, 2013 | 27 | 2013 |
