Junfeng Wen

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 4 papers; trusts 159 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #30,556 of 35,345 by trust here, #28,481 by citations
• Representation & Self-Supervised Learning — 48 citations into it
• Computer Vision and Pattern Recognition — 37 citations into it; #18,863 of 21,575 by trust here, #19,310 by citations
• Reinforcement Learning — 21 citations into it
• Probabilistic & Bayesian Methods — 19 citations into it
• Statistics and Probability — 16 citations into it
• Statistical Learning Theory — 15 citations into it
• Management Science and Operations Research — 11 citations into it

Whom Junfeng Wen trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Diederik P. Kingma
4. Shai Ben-David
5. Masashi Sugiyama
6. Yoshua Bengio
7. Lotfi A. Zadeh
8. Yann LeCun
9. Richard S. Sutton
10. Jimmy Ba

Who trusts Junfeng Wen overall

1. Shiori Sagawa
2. Seyed Iman Mirzadeh
3. Mengmi Zhang
4. Wouter M. Kouw
5. Mehrdad Farajtabar
6. Sana Tonekaboni
7. Kun Kuang
8. Yao Liu
9. Sharan Vaswani
10. Michael R. Zhang

Papers from Junfeng Wen's perspective

Ordered by how strongly the researchers Junfeng Wen trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Few-Shot Self Reminder to Overcome Catastrophic Forgetting (2018) — 24 citations
• Batch Stationary Distribution Estimation (2020) — 26 citations
• Domain Aggregation Networks for Multi-Source Domain Adaptation (2019) — 85 citations
• Robust Learning under Uncertain Test Distributions: Relating Covariate Shift to Model Misspecification (2014) — 118 citations