Mingda Chen

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 5 papers; trusts 270 researchers and is trusted by 106 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 196 citations into it; #2,851 of 35,345 by trust here, #1,476 by citations
• Natural Language Processing — 147 citations into it
• Computer Vision and Pattern Recognition — 86 citations into it; #3,975 of 21,575 by trust here, #1,129 by citations
• Evaluation & Benchmarks — 57 citations into it
• Large Language Models — 42 citations into it; #140 of 799 by trust here, #146 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Model Architectures — 18 citations into it
• Multimodal Learning — 14 citations into it

Whom Mingda Chen trusts overall

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

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

Who trusts Mingda Chen overall

1. Yi Tay
2. Mostafa Dehghani
3. Kalpesh Krishna
4. Dan Hendrycks
5. Yoon Kim
6. Mohit Bansal
7. Canwen Xu
8. Zexuan Zhong
9. Eric Wallace
10. Qingxiu Dong

Papers from Mingda Chen's perspective

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

Their papers

• ALBERT: A Lite BERT for Self-supervised Learning of Language Representations (2019) — 7,601 citations
• Controllable Paraphrase Generation with a Syntactic Exemplar (2019) — 116 citations
• Chameleon: Mixed-Modal Early-Fusion Foundation Models (2024) — 918 citations
• SummScreen: A Dataset for Abstractive Screenplay Summarization (2021) — 104 citations
• Improving In-Context Few-Shot Learning via Self-Supervised Training (2022) — 82 citations