Yu Zhang

Representation & Self-Supervised Learning · with Han · 2014–2016

A researcher in the Science corpus: 4 papers; trusts 90 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Representation & Self-Supervised Learning — 39 citations into it; #2,019 of 3,145 by trust here, #2,624 by citations
• Artificial Intelligence — 32 citations into it; #7,848 of 35,345 by trust here, #13,163 by citations
• Computational Mechanics — 24 citations into it; #1,802 of 10,001 by trust here, #2,407 by citations
• Optimization & Training — 20 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Statistical Learning Theory — 14 citations into it
• Statistics and Probability — 11 citations into it
• Computer Networks and Communications — 8 citations into it

Whom Yu Zhang trusts overall

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

1. Lei Han
2. Robert Tibshirani
3. R. Caruana
4. Jieping Ye
5. Jianhui Chen
6. Alon Zweig
7. Jun Liu
8. Yu Zhang
9. Seyoung Kim
10. Ali Jalali

Who trusts Yu Zhang overall

1. Yu Zhang
2. Lei Han
3. Qiang Yang
4. Jingwen Ye
5. Jianhang Zhou
6. Yutao Huang
7. Garrett Wilson
8. Sebastian Ruder

Papers from Yu Zhang's perspective

Ordered by how strongly the researchers Yu 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Multi-Stage Multi-Task Learning with Reduced Rank (2016) — 66 citations
• Learning Tree Structure in Multi-Task Learning (2015) — 77 citations
• Learning Multi-Level Task Groups in Multi-Task Learning (2015) — 78 citations
• Encoding Tree Sparsity in Multi-Task Learning: A Probabilistic Framework (2014) — 43 citations