Yichen Wang

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 4 papers; trusts 33 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 43 citations into it; #17,367 of 35,345 by trust here, #15,236 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Information Systems — 18 citations into it
• Computational Mathematics — 13 citations into it
• Computational Mechanics — 11 citations into it
• Management Science and Operations Research — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Graph Learning — 9 citations into it

Whom Yichen Wang trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Tamara G. Kolda
4. Kaiming He
5. Eric C. Chi
6. Alex Krizhevsky
7. Tomas Mikolov
8. Ilya Sutskever
9. Karen Simonyan
10. Diederik P. Kingma

Who trusts Yichen Wang overall

1. Woojeong Jin
2. Yejin Kim
3. Nan Du
4. Jimeng Sun
5. How Jing
6. Hongyuan Mei
7. Rakshit S. Trivedi
8. Seyed Mehran Kazemi
9. Rishab Goel
10. Srijan Kumar

Papers from Yichen Wang's perspective

Ordered by how strongly the researchers Yichen Wang 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs (2017) — 583 citations
• Rubik: Knowledge Guided Tensor Factorization and Completion for Health Data Analytics (2015) — 179 citations
• Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems (2019) — 143 citations
• Time-Sensitive Recommendation From Recurrent User Activities (2015) — 137 citations