Yujia Li

Affiliations:
  • DeepMind, London, UK
  • University of Toronto, Department of Computer Science, Canada


According to our database1, Yujia Li authored at least 36 papers between 2013 and 2023.

Collaborative distances:

Timeline

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Bibliography

2023
Faster sorting algorithms discovered using deep reinforcement learning.
Nat., 2023

Solving MaxSAT with Matrix Multiplication.
CoRR, 2023

Transformers Meet Directed Graphs.
Proceedings of the International Conference on Machine Learning, 2023

2022
Competition-Level Code Generation with AlphaCode.
CoRR, 2022

2021
Scaling Language Models: Methods, Analysis & Insights from Training Gopher.
CoRR, 2021

WikiGraphs: A Wikipedia Text - Knowledge Graph Paired Dataset.
CoRR, 2021

ETA Prediction with Graph Neural Networks in Google Maps.
Proceedings of the CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1, 2021

2020
Solving Mixed Integer Programs Using Neural Networks.
CoRR, 2020

Strong Generalization and Efficiency in Neural Programs.
CoRR, 2020

Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs.
Proceedings of the 8th International Conference on Learning Representations, 2020

2019
Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving.
CoRR, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.
CoRR, 2019

REGAL: Transfer Learning For Fast Optimization of Computation Graphs.
CoRR, 2019

Efficient Graph Generation with Graph Recurrent Attention Networks.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Learning Transferable Graph Exploration.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Graph Matching Networks for Learning the Similarity of Graph Structured Objects.
Proceedings of the 36th International Conference on Machine Learning, 2019

CompILE: Compositional Imitation Learning and Execution.
Proceedings of the 36th International Conference on Machine Learning, 2019

Deep reinforcement learning with relational inductive biases.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Compositional Imitation Learning: Explaining and executing one task at a time.
CoRR, 2018

Relational Deep Reinforcement Learning.
CoRR, 2018

Relational inductive biases, deep learning, and graph networks.
CoRR, 2018

Learning Deep Generative Models of Graphs.
CoRR, 2018

2017
Building More Expressive Structured Models.
PhD thesis, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.
CoRR, 2017

Learning model-based planning from scratch.
CoRR, 2017

Imagination-Augmented Agents for Deep Reinforcement Learning.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Dualing GANs.
Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016
The Variational Fair Autoencoder.
Proceedings of the 4th International Conference on Learning Representations, 2016

Gated Graph Sequence Neural Networks.
Proceedings of the 4th International Conference on Learning Representations, 2016

Understanding the Effective Receptive Field in Deep Convolutional Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015
Generative Moment Matching Networks.
Proceedings of the 32nd International Conference on Machine Learning, 2015

Feedback-based handwriting recognition from inertial sensor data for wearable devices.
Proceedings of the 2015 IEEE International Conference on Acoustics, 2015

2014
Mean-Field Networks.
CoRR, 2014

Learning unbiased features.
CoRR, 2014

High Order Regularization for Semi-Supervised Learning of Structured Output Problems.
Proceedings of the 31th International Conference on Machine Learning, 2014

2013
Exploring Compositional High Order Pattern Potentials for Structured Output Learning.
Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013


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