Vinícius Flores Zambaldi

Artificial Intelligence · 2017–2020

A researcher in the Science corpus: 14 papers; trusts 191 researchers and is trusted by 27 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 450 citations into it; #20,816 of 35,345 by trust here, #5,002 by citations
• Reinforcement Learning — 279 citations into it; #611 of 2,242 by trust here, #264 by citations
• Computer Vision and Pattern Recognition — 135 citations into it
• Management Science and Operations Research — 120 citations into it
• Game & Decision Theory — 113 citations into it; #126 of 249 by trust here, #49 by citations
• Model Architectures — 70 citations into it
• Natural Language Processing — 60 citations into it
• Graph Learning — 59 citations into it

Whom Vinícius Flores Zambaldi trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Richard S. Sutton
4. Yoshua Bengio
5. Kaiming He
6. Jimmy Ba
7. Alex Krizhevsky
8. Alex Graves
9. Ilya Sutskever
10. Yann LeCun

Who trusts Vinícius Flores Zambaldi overall

1. Sjoerd van Steenkiste
2. Jakob N. Foerster
3. Yuxi Li
4. Jiechuan Jiang
5. Andrea Tacchetti
6. Julien Pérolat
7. Richard Ryan Williams
8. Alex Peysakhovich
9. J. Urgen Schmidhuber
10. Thanh Thi Nguyen

Papers from Vinícius Flores Zambaldi's perspective

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

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

Their papers

• Relational Deep Reinforcement Learning (2018) — 245 citations
• A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning (2017) — 750 citations
• Compositional Imitation Learning: Explaining and executing one task at a time (2018) — 13 citations
• MEMO: A Deep Network for Flexible Combination of Episodic Memories (2020) — 40 citations
• Actor-Critic Policy Optimization in Partially Observable Multiagent Environments (2018) — 159 citations
• A multi-agent reinforcement learning model of common-pool resource appropriation (2017) — 225 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations
• Relational inductive biases, deep learning, and graph networks (2018) — 3,656 citations
• Relational Forward Models for Multi-Agent Learning (2018) — 85 citations
• Deep reinforcement learning with relational inductive biases (2018) — 231 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning Based On Team Reward (2018) — 895 citations
• CompILE: Compositional Imitation Learning and Execution (2018) — 139 citations
• Value-Decomposition Networks For Cooperative Multi-Agent Learning (2017) — 1,350 citations
• Multi-agent Reinforcement Learning in Sequential Social Dilemmas (2017) — 712 citations