Marvin Zhang

Artificial Intelligence · 2015–2021

A researcher in the Science corpus: 10 papers; trusts 496 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 265 citations into it; #5,171 of 35,345 by trust here, #6,157 by citations
• Reinforcement Learning — 180 citations into it; #503 of 2,242 by trust here, #709 by citations
• Control and Systems Engineering — 95 citations into it; #6,114 of 8,257 by trust here, #1,409 by citations
• Computer Vision and Pattern Recognition — 94 citations into it; #20,825 of 21,575 by trust here, #14,151 by citations
• Representation & Self-Supervised Learning — 50 citations into it; #2,132 of 3,145 by trust here, #1,692 by citations
• Computational Theory and Mathematics — 34 citations into it; #2,663 of 12,182 by trust here, #2,104 by citations
• Biomedical Engineering — 33 citations into it
• Robotics & Control — 28 citations into it

Whom Marvin Zhang trusts overall

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

1. Sergey Levine
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Geoffrey E. Hinton
5. Kaiming He
6. Yoshua Bengio
7. Jan Peters
8. Yann LeCun
9. Pieter Abbeel
10. Ilya Sutskever

Who trusts Marvin Zhang overall

1. Sergey Levine
2. Lei Tai
3. Pieter Abbeel
4. Dibya Ghosh
5. Shuaicheng Niu
6. Alex X. Lee
7. Nicolas Manfred Otto Heess
8. Carlos Florensa
9. Danijar Hafner
10. Carles Gelada

Papers from Marvin Zhang's perspective

Ordered by how strongly the researchers Marvin Zhang 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• When to Trust Your Model: Model-Based Policy Optimization (2019) — 1,258 citations
• AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos (2019) — 188 citations
• SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning (2018) — 42 citations
• Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning (2017) — 168 citations
• Learning deep neural network policies with continuous memory states (2015) — 95 citations
• Policy Learning with Continuous Memory States for Partially Observed Robotic Control (2015) — 19 citations
• Deep reinforcement learning for tensegrity robot locomotion (2016) — 106 citations
• MEMO: Test Time Robustness via Adaptation and Augmentation (2021) — 543 citations
• Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift (2020) — 88 citations
• SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning (2018) — 236 citations