| Gated graph sequence neural networks Y Li, D Tarlow, M Brockschmidt, R Zemel arXiv preprint arXiv:1511.05493, 2015 | 1408 | 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 | 1052 | 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 | 595 | 2016 |
| Generative moment matching networks Y Li, K Swersky, R Zemel International Conference on Machine Learning, 1718-1727, 2015 | 572 | 2015 |
| Imagination-Augmented Agents for Deep Reinforcement Learning T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv:1707.06203, 2017 | 371* | 2017 |
| The variational fair autoencoder C Louizos, K Swersky, Y Li, M Welling, R Zemel arXiv preprint arXiv:1511.00830, 2015 | 317 | 2015 |
| Learning deep generative models of graphs Y Li, O Vinyals, C Dyer, R Pascanu, P Battaglia arXiv preprint arXiv:1803.03324, 2018 | 262 | 2018 |
| Relational deep reinforcement learning V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ... arXiv preprint arXiv:1806.01830, 2018 | 128 | 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 | 95 | 2019 |
| Learning Model-Based Planning from Scratch R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ... arXiv:1707.06170, 2017 | 80 | 2017 |
| 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 | 72 | 2018 |
| 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 |
| Efficient graph generation with graph recurrent attention networks R Liao, Y Li, Y Song, S Wang, C Nash, WL Hamilton, D Duvenaud, ... arXiv preprint arXiv:1910.00760, 2019 | 49 | 2019 |
| 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 | 31* | 2018 |
| Mean Field Networks Y Li, R Zemel ICML workshop on Learning Tractable Probabilistic Models, 2014 | 31 | 2014 |
| High Order Regularization for Semi-Supervised Learning of Structured Output Problems Y Li, R Zemel International Conference on Machine Learning (ICML), 2014 | 26 | 2014 |
| Celebrity Recommendation with Collaborative Social Topic Regression. X Ding, X Jin, Y Li, L Li IJCAI, 2612-2618, 2013 | 24 | 2013 |
| Learning unbiased features Y Li, K Swersky, R Zemel NIPS workshop on Transfer and Multi-Task Learnnig, 2014 | 22 | 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 | 18 | 2019 |
| Dualing gans Y Li, A Schwing, KC Wang, R Zemel Advances in Neural Information Processing Systems 30, 5606-5616, 2017 | 17 | 2017 |
