Miao Liu
Artificial Intelligence · with How · 2015–2022
A researcher in the Science corpus: 15 papers; trusts 180 researchers and is trusted by 45 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 238 citations into it; #5,379 of 35,345 by trust here, #2,914 by citations
- Reinforcement Learning — 181 citations into it
- Computer Vision and Pattern Recognition — 69 citations into it; #5,324 of 21,575 by trust here, #2,754 by citations
- Management Science and Operations Research — 51 citations into it
- Representation & Self-Supervised Learning — 39 citations into it; #1,104 of 3,145 by trust here, #539 by citations
- Optimization & Training — 36 citations into it
- Model Architectures — 34 citations into it
- Computational Theory and Mathematics — 28 citations into it
Whom Miao Liu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Richard S. Sutton
- Geoffrey E. Hinton
- Yoshua Bengio
- J. Urgen Schmidhuber
- Volodymyr Mnih
- Diederik P. Kingma
- Alex Krizhevsky
- Alex Graves
- Andrew Barto
- Yann LeCun
Who trusts Miao Liu overall
- Matthew Riemer
- Shayegan Omidshafiei
- Alessandro Panella
- Tingxiang Fan
- Michael Everett
- Chris Amato
- Pinxin Long
- Jonathan P. How
- Kaiqing Zhang
- Yu Fan Chen
Papers from Miao Liu's perspective
Ordered by how strongly the researchers Miao Liu trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Learning Multiple Layers of Features from Tiny Images
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
Their papers
- Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (2018) — 934 citations
- Decentralized non-communicating multiagent collision avoidance with deep reinforcement learning (2016) — 697 citations
- Eigenoption Discovery through the Deep Successor Representation (2017) — 172 citations
- Socially aware motion planning with deep reinforcement learning (2017) — 777 citations
- Learning Hierarchical Teaching Policies for Cooperative Agents (2019) — 33 citations
- On the Role of Weight Sharing During Deep Option Learning (2019) — 22 citations
- A Policy Gradient Algorithm for Learning to Learn in Multiagent Reinforcement Learning (2020) — 69 citations
- Influencing Long-Term Behavior in Multiagent Reinforcement Learning (2022) — 28 citations
- Context-Specific Representation Abstraction for Deep Option Learning (2021) — 16 citations
- Learning to Teach in Cooperative Multiagent Reinforcement Learning (2018) — 145 citations
- Stick-Breaking Policy Learning in Dec-POMDPs (2015) — 31 citations
- Learning Abstract Options (2018) — 85 citations
- Learning for multi-robot cooperation in partially observable stochastic environments with macro-actions (2017) — 34 citations
- Deep Q-Networks for Accelerating the Training of Deep Neural Networks (2016) — 12 citations
- Learning for Decentralized Control of Multiagent Systems in Large, Partially-Observable Stochastic Environments (2016) — 36 citations