Fengxiang He

Artificial Intelligence · 2019–2021

A researcher in the Science corpus: 5 papers; trusts 211 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it
• Computer Vision and Pattern Recognition — 98 citations into it
• Optimization & Training — 37 citations into it
• Scaling & Learning Theory — 30 citations into it
• Representation & Self-Supervised Learning — 23 citations into it
• Model Architectures — 21 citations into it
• Statistical Learning Theory — 14 citations into it
• Computational Mechanics — 10 citations into it

Whom Fengxiang He trusts overall

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

1. Alex Krizhevsky
2. Tong Zhang
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Kaiming He
6. Ilya Sutskever
7. Diederik P. Kingma
8. Alex Graves
9. Yann LeCun
10. L. Eon Bottou

Who trusts Fengxiang He overall

1. Jianping Gou
2. Baosheng Yu

Papers from Fengxiang He's perspective

Ordered by how strongly the researchers Fengxiang He 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. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Visual Semantics Allow for Textual Reasoning Better in Scene Text Recognition (2021) — 67 citations
• Artificial Neural Variability for Deep Learning: On Overfitting, Noise Memorization, and Catastrophic Forgetting (2020) — 71 citations
• Understanding Generalization in Recurrent Neural Networks (2020) — 34 citations
• Collect and Select: Semantic Alignment Metric Learning for Few-Shot Learning (2019) — 149 citations
• Why ResNet Works? Residuals Generalize (2019) — 373 citations