Yi-Lin Sung

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 3 papers; trusts 713 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 125 citations into it; #10,860 of 35,345 by trust here, #14,181 by citations
• Computer Vision and Pattern Recognition — 92 citations into it; #7,385 of 21,575 by trust here, #9,722 by citations
• Efficiency & Compression — 44 citations into it; #496 of 1,264 by trust here, #840 by citations
• Natural Language Processing — 38 citations into it
• Large Language Models — 31 citations into it
• Multimodal Learning — 27 citations into it; #769 of 1,211 by trust here, #952 by citations
• Model Architectures — 20 citations into it
• Optimization & Training — 19 citations into it

Whom Yi-Lin Sung trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Diederik P. Kingma
6. Yann LeCun
7. Ilya Sutskever
8. Ross B. Girshick
9. Shaoqing Ren
10. Karen Simonyan

Who trusts Yi-Lin Sung overall

1. Zeyu Han
2. Jaemin Cho
3. Peng Gao
4. Lingling Xu
5. Mohit Bansal
6. Lvmin Zhang
7. Chao Gao
8. Tim Dettmers
9. Zihao Fu
10. Zhe Chen

Papers from Yi-Lin Sung's perspective

Ordered by how strongly the researchers Yi-Lin Sung 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. 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• VL-ADAPTER: Parameter-Efficient Transfer Learning for Vision-and-Language Tasks (2021) — 499 citations
• Training Neural Networks with Fixed Sparse Masks (2021) — 282 citations
• LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning (2022) — 329 citations