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Yujia Li
Yujia Li
Research Scientist, DeepMind
Dirección de correo verificada de google.com - Página principal
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Citado por
Citado por
Año
Gated graph sequence neural networks
Y Li, D Tarlow, M Brockschmidt, R Zemel
arXiv preprint arXiv:1511.05493, 2015
25152015
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
20812018
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
11142016
Generative moment matching networks
Y Li, K Swersky, R Zemel
International conference on machine learning, 1718-1727, 2015
7922015
Imagination-Augmented Agents for Deep Reinforcement Learning
T Weber, S Racanière, DP Reichert, L Buesing, A Guez, DJ Rezende, ...
arXiv:1707.06203, 2017
524*2017
The variational fair autoencoder
C Louizos, K Swersky, Y Li, M Welling, R Zemel
arXiv preprint arXiv:1511.00830, 2015
5032015
Learning deep generative models of graphs
Y Li, O Vinyals, C Dyer, R Pascanu, P Battaglia
arXiv preprint arXiv:1803.03324, 2018
4592018
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
2982019
Relational deep reinforcement learning
V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ...
arXiv preprint arXiv:1806.01830, 2018
2052018
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
1692019
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
1332018
Learning Model-Based Planning from Scratch
R Pascanu, Y Li, O Vinyals, N Heess, L Buesing, S Racanière, D Reichert, ...
arXiv:1707.06170, 2017
1022017
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
1002021
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
73*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
702020
Competition-level code generation with alphacode
Y Li, D Choi, J Chung, N Kushman, J Schrittwieser, R Leblond, T Eccles, ...
arXiv preprint arXiv:2203.07814, 2022
55*2022
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
522013
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
422019
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
382019
Mean Field Networks
Y Li, R Zemel
ICML workshop on Learning Tractable Probabilistic Models, 2014
342014
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20