Di Huang

Artificial Intelligence · with Jiang · 2019

A researcher in the Science corpus: 2 papers; trusts 310 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 81 citations into it
• Computer Vision and Pattern Recognition — 58 citations into it
• Optimization & Training — 35 citations into it
• Safety, Robustness & Interpretability — 18 citations into it
• Evaluation & Benchmarks — 13 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Civil and Structural Engineering — 8 citations into it
• Model Architectures — 8 citations into it

Whom Di Huang trusts overall

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

1. Geoffrey E. Hinton
2. Christian Szegedy
3. Alex Krizhevsky
4. Kaiming He
5. Sergey Ioffe
6. Yoshua Bengio
7. Jia Deng
8. Ilya Sutskever
9. Ross B. Girshick
10. Diederik P. Kingma

Who trusts Di Huang overall

1. Pierre Foret
2. Junnan Li
3. Ariel Kleiner
4. Jungmin Kwon
5. Hung-Yu Tseng

Papers from Di Huang's perspective

Ordered by how strongly the researchers Di Huang 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Synthetic vs Real: Deep Learning on Controlled Noise (2019) — 3 citations
• Beyond Synthetic Noise: Deep Learning on Controlled Noisy Labels (2019) — 252 citations