Jianye Hao

Artificial Intelligence · with Chen · 2018–2021

A researcher in the Science corpus: 13 papers; trusts 175 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 220 citations into it; #16,428 of 35,345 by trust here, #12,974 by citations
• Reinforcement Learning — 186 citations into it
• Computer Vision and Pattern Recognition — 69 citations into it; #16,695 of 21,575 by trust here, #19,310 by citations
• Representation & Self-Supervised Learning — 39 citations into it
• Computational Theory and Mathematics — 34 citations into it; #7,321 of 12,182 by trust here, #7,851 by citations
• Management Science and Operations Research — 32 citations into it
• Electrical and Electronic Engineering — 26 citations into it
• Game & Decision Theory — 25 citations into it

Whom Jianye Hao trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Geoffrey E. Hinton
2. Richard S. Sutton
3. Alex Krizhevsky
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Alex Graves
8. J. Urgen Schmidhuber
9. Kaiming He
10. Volodymyr Mnih

Who trusts Jianye Hao overall

1. Tabish Rashid
2. Liyuan Wang
3. Jianhao Wang
4. Pablo Hernandez-Leal
5. Gregory Palmer
6. Sanjeevan Ahilan

Papers from Jianye Hao's perspective

Ordered by how strongly the researchers Jianye Hao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• From Few to More: Large-scale Dynamic Multiagent Curriculum Learning (2019) — 141 citations
• CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models (2020) — 407 citations
• Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive Learning (2020) — 61 citations
• Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces (2019) — 84 citations
• Weighted Double Deep Multiagent Reinforcement Learning in Stochastic Cooperative Environments (2018) — 38 citations
• Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (2021) — 110 citations
• Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse (2020) — 335 citations
• Q-value Path Decomposition for Deep Multiagent Reinforcement Learning (2020) — 65 citations
• Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning (2020) — 236 citations
• Action Semantics Network: Considering the Effects of Actions in Multiagent Systems (2019) — 46 citations
• Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning (2019) — 85 citations
• Multi-Agent Game Abstraction via Graph Attention Neural Network (2019) — 332 citations
• Hierarchical Deep Multiagent Reinforcement Learning (2018) — 24 citations