Richong Zhang

Artificial Intelligence · with Mao · 2016–2020

A researcher in the Science corpus: 4 papers; trusts 193 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 87 citations into it; #17,497 of 35,345 by trust here, #14,438 by citations
• Computer Vision and Pattern Recognition — 48 citations into it; #11,039 of 21,575 by trust here, #11,950 by citations
• Optimization & Training — 30 citations into it; #1,782 of 2,601 by trust here, #1,715 by citations
• Natural Language Processing — 20 citations into it
• Model Architectures — 17 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Scaling & Learning Theory — 11 citations into it
• Safety, Robustness & Interpretability — 6 citations into it

Whom Richong Zhang trusts overall

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

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

Who trusts Richong Zhang overall

1. Hongyu Guo
2. David Stutz
3. Jang-Hyun Kim

Papers from Richong Zhang's perspective

Ordered by how strongly the researchers Richong Zhang 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. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• MixUp as Locally Linear Out-Of-Manifold Regularization (2018) — 353 citations
• Regularizing Neural Networks via Adversarial Model Perturbation (2020) — 116 citations
• Aspect-Level Sentiment Analysis Via Convolution over Dependency Tree (2019) — 377 citations
• On the Representation and Embedding of Knowledge Bases beyond Binary Relations (2016) — 153 citations