Yutai Hou

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 182 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 165 citations into it; #23,092 of 35,345 by trust here, #12,083 by citations
• Natural Language Processing — 116 citations into it; #3,877 of 5,688 by trust here, #3,687 by citations
• Computer Vision and Pattern Recognition — 75 citations into it; #17,320 of 21,575 by trust here, #12,689 by citations
• Representation & Self-Supervised Learning — 39 citations into it
• Optimization & Training — 17 citations into it
• Statistical and Nonlinear Physics — 6 citations into it
• Management Science and Operations Research — 5 citations into it

Whom Yutai Hou trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Alex Krizhevsky
5. Ilya Sutskever
6. Yann LeCun
7. Jimmy Ba
8. Richard Socher
9. Oriol Vinyals
10. Kaiming He

Who trusts Yutai Hou overall

1. Kang Min Yoo
2. Xiang Dai
3. Freda Shi
4. Zijian Zhao
5. Biswesh Mohapatra
6. Silin Gao
7. Panupong Pasupat
8. Yichun Yin
9. Sha Li
10. Hengyi Cai

Papers from Yutai Hou's perspective

Ordered by how strongly the researchers Yutai Hou 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. 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020) — 270 citations
• Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network (2020) — 207 citations
• Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (2018) — 160 citations
• Data Augmentation Approaches in Natural Language Processing: A Survey (2021) — 386 citations