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Yujia Li
Researcher at Google
Publications - 57
Citations - 11220
Yujia Li is an academic researcher from Google. The author has contributed to research in topics: Artificial neural network & Reinforcement learning. The author has an hindex of 27, co-authored 56 publications receiving 8163 citations. Previous affiliations of Yujia Li include University of Toronto.
Papers
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Proceedings Article
Gated Graph Sequence Neural Networks.
TL;DR: This work studies feature learning techniques for graph-structured inputs and achieves state-of-the-art performance on a problem from program verification, in which subgraphs need to be matched to abstract data structures.
Posted Content
Relational inductive biases, deep learning, and graph networks
Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,H. Francis Song,Andrew J. Ballard,Justin Gilmer,George E. Dahl,Ashish Vaswani,Kelsey R. Allen,Charlie Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matthew Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu +26 more
TL;DR: It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.
Posted Content
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
TL;DR: In this paper, the authors introduce the notion of an effective receptive field, and show that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field.
Proceedings Article
Understanding the effective receptive field in deep convolutional neural networks
TL;DR: The notion of an effective receptive fieldsize is introduced, and it is shown that it both has a Gaussian distribution and only occupies a fraction of the full theoretical receptive field size.
Posted Content
Generative Moment Matching Networks
TL;DR: In this paper, a generative adversarial network (GAN) was proposed to generate an independent sample via a single feedforward pass through a multilayer perceptron, which can be trained by backpropagation.