Chongjie Zhang
Artificial Intelligence · 2010–2021
A researcher in the Science corpus: 14 papers; trusts 235 researchers and is trusted by 49 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 312 citations into it; #4,270 of 35,345 by trust here, #7,062 by citations
- Reinforcement Learning — 278 citations into it; #453 of 2,242 by trust here, #756 by citations
- Management Science and Operations Research — 68 citations into it; #4,584 of 11,946 by trust here, #5,316 by citations
- Computational Theory and Mathematics — 58 citations into it; #8,954 of 12,182 by trust here, #5,239 by citations
- Computer Networks and Communications — 41 citations into it; #1,969 of 9,886 by trust here, #3,074 by citations
- Game & Decision Theory — 31 citations into it
- Electrical and Electronic Engineering — 28 citations into it
- Computer Vision and Pattern Recognition — 27 citations into it; #16,624 of 21,575 by trust here, #13,542 by citations
Whom Chongjie Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Carlos Guestrin
- Michael H. Bowling
- Richard S. Sutton
- Martin L. Puterman
- Jakob N. Foerster
- Satinder Singh
- Victor R. Lesser
- Geoffrey E. Hinton
- Tabish Rashid
- Diederik P. Kingma
Who trusts Chongjie Zhang overall
- Tonghan Wang
- Jianhao Wang
- Jakob N. Foerster
- Yihan Wang
- Frans Adriaan Oliehoek
- Pablo Hernandez-Leal
- Sainbayar Sukhbaatar
- Beining Han
- Victor R. Lesser
- Luke Metz
Papers from Chongjie Zhang's perspective
Ordered by how strongly the researchers Chongjie Zhang 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)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- Reinforcement Learning: An Introduction
- Human-level control through deep reinforcement learning
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Multi-Agent Learning with Policy Prediction (2010) — 144 citations
- Coordinating multi-agent reinforcement learning with limited communication (2013) — 155 citations
- Context-Aware Policy Reuse (2018) — 41 citations
- Celebrating Diversity in Shared Multi-Agent Reinforcement Learning (2021) — 208 citations
- RODE: Learning Roles to Decompose Multi-Agent Tasks (2020) — 286 citations
- Coordinated Multi-Agent Reinforcement Learning in Networked Distributed POMDPs (2011) — 137 citations
- Influence-Based Multi-Agent Exploration (2019) — 160 citations
- Off-Policy Multi-Agent Decomposed Policy Gradients (2020) — 220 citations
- Learning Nearly Decomposable Value Functions Via Communication Minimization (2019) — 179 citations
- QPLEX: Duplex Dueling Multi-Agent Q-Learning (2020) — 633 citations
- ROMA: Multi-Agent Reinforcement Learning with Emergent Roles (2020) — 304 citations
- Towards Understanding Linear Value Decomposition in Cooperative Multi-Agent Q-Learning (2020) — 25 citations
- Fairness in Multi-Agent Sequential Decision-Making (2014) — 70 citations
- Self-organization for coordinating decentralized reinforcement learning (2010) — 42 citations