Author

Yujia Li

Research Scientist, Google DeepMind - Cited by 13,285 - Machine Learning - Computer Vision - Natural Language Processing - Optimization

Biography

 Mr. Yojia Li is currently working at Chinese Academy of Sciences, China. He completed his previous education from University of Toronto. His research is focussed on Hepatitis, HCV infection, anti-HCV therapy and HBV replication
Title
Cited by
Year
Gated graph sequence neural networks
Y Li, D Tarlow, M Brockschmidt, R ZemelarXiv preprint arXiv:1511.05493, 2015201
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, 2018201
2018
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
W Luo, Y Li, R Urtasun, R ZemelAdvances in Neural Information Processing Systems (NIPS), 2016201
2016
Generative moment matching networks
Y Li, K Swersky, R ZemelInternational conference on machine learning, 1718-1727, 2015201
914
2015
Imagination-Augmented Agents for Deep Reinforcement Learning
T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ...arXiv:1707.06203, 2017614201
614
2017
The variational fair autoencoder
C Louizos, K Swersky, Y Li, M Welling, R ZemelarXiv preprint arXiv:1511.00830, 2015201
601
2015
Learning deep generative models of graphs
Y Li, O Vinyals, C Dyer, R Pascanu, P BattagliaarXiv preprint arXiv:1803.03324, 2018201
585
2018
Graph matching networks for learning the similarity of graph structured objects
Y Li, C Gu, T Dullien, O Vinyals, P KohliInternational conference on machine learning, 3835-3845, 2019201
444
2019
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, 2021202
394
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, 2022346202
346
2022
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, 2019201
248
2019
Relational deep reinforcement learning
V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ...arXiv preprint arXiv:1806.01830, 2018201
240
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 DaiProceedings of the AAAI conference on artificial intelligence 34 (01), 606-613, 2020184202
184
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, 2018201
172
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, 2020202
143
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 …, 2021202
134
2021
Learning Model-Based Planning from Scratch
R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ...arXiv:1707.06170, 2017201
113
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 BattagliaarXiv preprint arXiv:1812.01483, 2018111201
111
2018
Reinforced genetic algorithm learning for optimizing computation graphs
A Paliwal, F Gimeno, V Nair, Y Li, M Lubin, P Kohli, O VinyalsarXiv preprint arXiv:1905.02494, 2019201
56
2019
Exploring compositional high order pattern potentials for structured output learning
Y Li, D Tarlow, R ZemelProceedings of the IEEE Conference on Computer Vision and Pattern …, 2013201
52
2013