Mu-Hwa Chen

Signal Processing · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Signal Processing — 25 citations into it; #2,519 of 7,572 by trust here, #2,589 by citations
• Management Science and Operations Research — 23 citations into it
• Artificial Intelligence — 18 citations into it; #19,607 of 35,345 by trust here, #19,018 by citations
• Model Architectures — 17 citations into it
• Time-Series & Forecasting — 15 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it
• Building and Construction — 7 citations into it
• Natural Language Processing — 6 citations into it

Whom Mu-Hwa Chen trusts overall

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

1. Dzmitry Bahdanau
2. Yoshua Bengio
3. Sean J. Taylor
4. S. Brandt
5. Guillaume Chevillon
6. Shaojie Bai
7. Geoffrey E. Hinton
8. Nikita Kitaev
9. Valentin Flunkert
10. Ashish Vaswani

Who trusts Mu-Hwa Chen overall

1. Zhijian Xu
2. Donghao Luo
3. Shiyu Wang
4. Yunhao Zhang
5. Gerald Woo
6. Haixu Wu
7. Nate Gruver
8. Yuqi Nie
9. Ailing Zeng
10. Vijayabharathi Ekambaram

Papers from Mu-Hwa Chen's perspective

Ordered by how strongly the researchers Mu-Hwa Chen 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet: A large-scale hierarchical image database

Their papers

• SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction (2021) — 851 citations
• Are Transformers Effective for Time Series Forecasting? (2022) — 4,138 citations