Fengda Zhu

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 426 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 115 citations into it; #30,536 of 35,345 by trust here, #17,110 by citations
• Computer Vision and Pattern Recognition — 101 citations into it; #18,711 of 21,575 by trust here, #10,905 by citations
• Reinforcement Learning — 72 citations into it
• Multimodal Learning — 27 citations into it; #1,010 of 1,211 by trust here, #892 by citations
• Natural Language Processing — 22 citations into it
• Representation & Self-Supervised Learning — 19 citations into it
• Management Science and Operations Research — 10 citations into it
• Electrical and Electronic Engineering — 9 citations into it

Whom Fengda Zhu trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Volodymyr Mnih
4. Alex Krizhevsky
5. Kaiming He
6. Diederik P. Kingma
7. Yann LeCun
8. Ilya Sutskever
9. Alex Graves
10. Ross B. Girshick

Who trusts Fengda Zhu overall

1. Xin Ye
2. Jiafei Duan
3. Jie Hong
4. Yi Zhu
5. Arjun Majumdar
6. Yezhou Yang
7. Samson Yu
8. Yicong Hong
9. Jialu Li
10. Pengfei Fang

Papers from Fengda Zhu's perspective

Ordered by how strongly the researchers Fengda Zhu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Vision-Language Navigation With Self-Supervised Auxiliary Reasoning Tasks (2019) — 286 citations
• Sim-Real Joint Reinforcement Transfer for 3D Indoor Navigation (2019) — 29 citations
• Vision-Dialog Navigation by Exploring Cross-Modal Memory (2020) — 54 citations
• Continual Reinforcement Learning with Diversity Exploration and Adversarial Self-Correction (2019) — 3 citations