Zhezhi He

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 156 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 59 citations into it; #20,428 of 35,345 by trust here, #25,040 by citations
• Computer Vision and Pattern Recognition — 37 citations into it; #17,111 of 21,575 by trust here, #17,757 by citations
• Safety, Robustness & Interpretability — 32 citations into it; #1,798 of 2,251 by trust here, #2,041 by citations
• Hardware and Architecture — 10 citations into it
• Signal Processing — 9 citations into it
• Model Architectures — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Zhezhi He trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yoshua Bengio
5. Christian Szegedy
6. Yann LeCun
7. Ian J. Goodfellow
8. Ilya Sutskever
9. Karen Simonyan
10. Diederik P. Kingma

Who trusts Zhezhi He overall

1. Naveed Akhtar
2. Dongxian Wu

Papers from Zhezhi He's perspective

Ordered by how strongly the researchers Zhezhi He trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Bit-Flip Attack: Crushing Neural Network With Progressive Bit Search (2019) — 330 citations
• Parametric Noise Injection: Trainable Randomness to Improve Deep Neural Network Robustness Against Adversarial Attack (2018) — 328 citations