Ying Wei

Artificial Intelligence · with Huang · 2016–2021

A researcher in the Science corpus: 14 papers; trusts 666 researchers and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 242 citations into it; #14,792 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 169 citations into it; #18,536 of 21,575 by trust here, #8,409 by citations
• Representation & Self-Supervised Learning — 110 citations into it
• Molecular Biology — 38 citations into it
• Graph Learning — 36 citations into it
• Optimization & Training — 35 citations into it
• Computational Theory and Mathematics — 33 citations into it
• Evaluation & Benchmarks — 28 citations into it

Whom Ying Wei trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Kaiming He
5. Diederik P. Kingma
6. Ilya Sutskever
7. Yann LeCun
8. Ruslan Salakhutdinov
9. Ross B. Girshick
10. Radford M. Neal

Who trusts Ying Wei overall

1. Han-Jia Ye
2. Yu Zhang
3. Cuong V Nguyen
4. B. Ermiş
5. Bin Guo
6. Qiang Yang
7. Anh Tuan Tran
8. Wei-Lun Chao
9. Xueyan Yin
10. Tal Hassner

Papers from Ying Wei's perspective

Ordered by how strongly the researchers Ying Wei 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Hierarchically Structured Meta-learning (2019) — 223 citations
• Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning (2016) — 34 citations
• Transferable Contextual Bandit for Cross-Domain Recommendation (2018) — 59 citations
• Deep Neural Networks for High Dimension, Low Sample Size Data (2017) — 222 citations
• Frustratingly Easy Transferability Estimation (2021) — 75 citations
• TranSlider: Transfer Ensemble Learning from Exploitation to Exploration (2020) — 13 citations
• Self-Supervised Graph Transformer on Large-Scale Molecular Data (2020) — 1,044 citations
• Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis (2019) — 188 citations
• Graph Few-shot Learning via Knowledge Transfer (2019) — 194 citations
• From Whole Slide Imaging to Microscopy: Deep Microscopy Adaptation Network for Histopathology Cancer Image Classification (2019) — 70 citations
• Transfer Learning via Learning to Transfer (2018) — 167 citations
• Learning to Multitask (2018) — 54 citations
• End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification (2017) — 256 citations
• Transfer Knowledge between Cities (2016) — 157 citations