Junjie Yang

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 5 papers; trusts 207 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 143 citations into it; #32,195 of 35,345 by trust here, #23,077 by citations
• Computer Vision and Pattern Recognition — 87 citations into it
• Optimization & Training — 37 citations into it
• Natural Language Processing — 32 citations into it
• Representation & Self-Supervised Learning — 17 citations into it
• Management Science and Operations Research — 16 citations into it
• Meta & Continual Learning — 13 citations into it
• Reinforcement Learning — 12 citations into it

Whom Junjie Yang trusts overall

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

1. Diederik P. Kingma
2. Chelsea Finn
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Krizhevsky
6. Sergey Levine
7. Jimmy Ba
8. Yann LeCun
9. Ilya Sutskever
10. Oriol Vinyals

Who trusts Junjie Yang overall

1. Kaiyi Ji
2. Ruichen Jiang
3. Petros Stavropoulos
4. Daniel Khashabi
5. Xiang Deng

Papers from Junjie Yang's perspective

Ordered by how strongly the researchers Junjie Yang 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. ImageNet Large Scale Visual Recognition Challenge
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Retrospective Reader for Machine Reading Comprehension (2020) — 241 citations
• Software-hardware co-design for fast and scalable training of deep learning recommendation models (2021) — 209 citations
• High-performance, Distributed Training of Large-scale Deep Learning Recommendation Models (2021) — 49 citations
• Bilevel Optimization: Convergence Analysis and Enhanced Design (2020) — 366 citations
• Multi-Step Model-Agnostic Meta-Learning: Convergence and Improved Algorithms (2020) — 52 citations