Yan Zhang

Artificial Intelligence · with Hare · 2018–2021

A researcher in the Science corpus: 8 papers; trusts 795 researchers and is trusted by 41 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 192 citations into it; #7,456 of 35,345 by trust here, #9,870 by citations
• Computer Vision and Pattern Recognition — 141 citations into it; #5,064 of 21,575 by trust here, #8,409 by citations
• Natural Language Processing — 67 citations into it
• Model Architectures — 49 citations into it; #946 of 2,278 by trust here, #1,882 by citations
• Representation & Self-Supervised Learning — 40 citations into it; #1,736 of 3,145 by trust here, #2,217 by citations
• Graph Learning — 32 citations into it
• Optimization & Training — 20 citations into it
• Evaluation & Benchmarks — 18 citations into it

Whom Yan Zhang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Kaiming He
5. Alex Krizhevsky
6. Ilya Sutskever
7. Yann LeCun
8. Oriol Vinyals
9. Alex Graves
10. Jimmy Ba

Who trusts Yan Zhang overall

1. David W. Zhang
2. Adam R. Kosiorek
3. Zhou Yu
4. Carl Yang
5. Qian Huang
6. Marco Tulio Ribeiro
7. Gertjan J. Burghouts
8. Swarnadeep Saha
9. Dalu Guo
10. Horace He

Papers from Yan Zhang's perspective

Ordered by how strongly the researchers Yan 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Learning to Count Objects in Natural Images for Visual Question Answering (2018) — 224 citations
• Deep Set Prediction Networks (2019) — 130 citations
• FSPool: Learning Set Representations with Featurewise Sort Pooling (2019) — 94 citations
• An Unsupervised Sentence Embedding Method by Mutual Information Maximization (2020) — 197 citations
• Attention Guided Graph Convolutional Networks for Relation Extraction (2019) — 461 citations
• Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation (2021) — 22 citations
• Set-Structured Latent Representations (2020)
• Better Set Representations For Relational Reasoning (2020) — 22 citations