Ruofeng Wen

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 14 researchers and is trusted by 20 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 14 citations into it; #8,240 of 35,345 by trust here, #14,718 by citations
• Computer Vision and Pattern Recognition — 12 citations into it; #14,552 of 21,575 by trust here, #20,756 by citations
• Generative Models — 12 citations into it
• Management Science and Operations Research — 8 citations into it; #2,700 of 11,946 by trust here, #3,153 by citations
• Statistics and Probability — 6 citations into it
• Economics and Econometrics — 5 citations into it
• Time-Series & Forecasting — 5 citations into it; #76 of 381 by trust here, #92 by citations

Whom Ruofeng Wen trusts overall

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

1. Yoshua Bengio
2. Guillaume Chevillon
3. Alex Graves
4. Diederik P. Kingma
5. Geoffrey E. Hinton
6. Ian J. Goodfellow
7. Jianqing Fan
8. Aäron van den Oord
9. Oriol Vinyals
10. Samy Bengio

Who trusts Ruofeng Wen overall

1. Bryan Lim
2. David Salinas
3. Jan Gasthaus
4. Konstantinos Benidis
5. Bernie Wang
6. Stefan Zohren
7. Syama Sundar Rangapuram
8. Tim Januschowski
9. Haixu Wu
10. Michael Bohlke-Schneider

Papers from Ruofeng Wen's perspective

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

Their papers

• A Multi-Horizon Quantile Recurrent Forecaster (2017) — 527 citations
• Deep Generative Quantile-Copula Models for Probabilistic Forecasting (2019) — 38 citations