Yun-Nung Chen

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 297 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 66 citations into it; #27,707 of 35,345 by trust here, #20,377 by citations
• Natural Language Processing — 41 citations into it; #4,583 of 5,688 by trust here, #3,977 by citations
• Computer Vision and Pattern Recognition — 35 citations into it; #17,097 of 21,575 by trust here, #14,151 by citations
• Large Language Models — 16 citations into it
• Model Architectures — 16 citations into it
• Meta & Continual Learning — 8 citations into it; #866 of 1,041 by trust here, #848 by citations
• Reinforcement Learning — 8 citations into it
• Evaluation & Benchmarks — 6 citations into it

Whom Yun-Nung Chen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Ilya Sutskever
4. Diederik P. Kingma
5. Kaiming He
6. Alec Radford
7. Alex Krizhevsky
8. Oriol Vinyals
9. Yann LeCun
10. Sepp Hochreiter

Who trusts Yun-Nung Chen overall

1. Sabeen Ahmed
2. Zixuan Ke

Papers from Yun-Nung Chen's perspective

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

Their papers

• Relating Neural Text Degeneration to Exposure Bias (2021) — 25 citations
• Lifelong Language Knowledge Distillation (2020) — 57 citations
• What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding (2020) — 116 citations