Dawei Feng

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 133 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 124 citations into it
• Reinforcement Learning — 84 citations into it
• Computer Vision and Pattern Recognition — 43 citations into it
• Control and Systems Engineering — 20 citations into it
• Representation & Self-Supervised Learning — 11 citations into it
• Signal Processing — 9 citations into it
• Evaluation & Benchmarks — 5 citations into it
• Robotics & Control — 5 citations into it

Whom Dawei Feng trusts overall

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

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

Papers from Dawei Feng's perspective

Ordered by how strongly the researchers Dawei Feng 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Going deeper with convolutions

Their papers

• Deep reinforcement learning: a survey (2020) — 780 citations
• An Adversarial Feature Distillation Method for Audio Classification (2019) — 24 citations
• Exploring Pre-trained Language Models for Event Extraction and Generation (2019) — 341 citations