Gyeong-In Yu

Artificial Intelligence · 2020–2022

A researcher in the Science corpus: 2 papers; trusts 126 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 49 citations into it
• Computer Vision and Pattern Recognition — 42 citations into it
• Large Language Models — 17 citations into it
• Natural Language Processing — 16 citations into it
• Efficiency & Compression — 15 citations into it
• Model Architectures — 13 citations into it
• Optimization & Training — 6 citations into it
• Multimodal Learning — 5 citations into it

Whom Gyeong-In Yu trusts overall

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

1. Kaiming He
2. Alex Krizhevsky
3. Ronald J. Williams
4. Geoffrey E. Hinton
5. Sepp Hochreiter
6. Yoshua Bengio
7. Ilya Sutskever
8. Jacob Devlin
9. Diederik P. Kingma
10. J. Urgen Schmidhuber

Who trusts Gyeong-In Yu overall

1. Ying Sheng
2. Yinmin Zhong
3. Amey Agrawal
4. Zichang Liu
5. Zhuohan Li
6. Lianmin Zheng
7. Esha Choukse
8. Shengyu Liu
9. Nitin Kedia
10. Binhang Yuan

Papers from Gyeong-In Yu's perspective

Ordered by how strongly the researchers Gyeong-In Yu 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. ImageNet: A large-scale hierarchical image database
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. ImageNet Large Scale Visual Recognition Challenge
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning (2020) — 92 citations
• Orca: A Distributed Serving System for Transformer-Based Generative Models (2022) — 785 citations