Dongsu Zhang

Artificial Intelligence · 2020

A researcher in the Science corpus: 1 paper; trusts 66 researchers and is trusted by 18 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it
• Representation & Self-Supervised Learning — 15 citations into it
• Meta & Continual Learning — 11 citations into it; #303 of 1,041 by trust here, #333 by citations
• Reinforcement Learning — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it; #647 of 1,525 by trust here, #477 by citations

Whom Dongsu Zhang trusts overall

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

1. Alex Krizhevsky
2. Rahaf Aljundi
3. Radford M. Neal
4. Geoffrey E. Hinton
5. Michael I. Jordan
6. Kaiming He
7. Yoshua Bengio
8. Marc'Aurelio Ranzato
9. Yann LeCun
10. Diederik P. Kingma

Who trusts Dongsu Zhang overall

1. Fei Ye
2. Zheda Mai
3. Adrian G. Bors
4. James Smith
5. Jorge Armando Mendez Mendez
6. Matthias De Lange
7. K. J. Joseph
8. Binh Tang
9. Nikhil Mehta

Papers from Dongsu Zhang's perspective

Ordered by how strongly the researchers Dongsu 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Learning Multiple Layers of Features from Tiny Images
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. ImageNet: A large-scale hierarchical image database
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning (2020) — 242 citations