Renqian Luo
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 8 papers; trusts 361 researchers and is trusted by 42 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 216 citations into it; #6,649 of 35,345 by trust here, #3,183 by citations
- Computer Vision and Pattern Recognition — 131 citations into it; #4,687 of 21,575 by trust here, #3,089 by citations
- Natural Language Processing — 86 citations into it
- Model Architectures — 57 citations into it; #602 of 2,278 by trust here, #706 by citations
- Neural Architecture Search & AutoML — 36 citations into it
- Optimization & Training — 27 citations into it
- Large Language Models — 24 citations into it
- Efficiency & Compression — 21 citations into it
Whom Renqian Luo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Barret Zoph
- Quoc V. Le
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Kaiming He
- Ilya Sutskever
- Karen Simonyan
- Yann LeCun
Who trusts Renqian Luo overall
- Xu Tan
- Jiahui Yu
- Quanming Yao
- Tao Qin
- Jianlong Chang
- Xuanyi Dong
- Miao Zhang
- Shen Yan
- Xinbang Zhang
- Thomas S. Huang
Papers from Renqian Luo's perspective
Ordered by how strongly the researchers Renqian Luo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- Learning Multiple Layers of Features from Tiny Images
Their papers
- Neural Architecture Optimization (2018) — 720 citations
- Achieving Human Parity on Automatic Chinese to English News Translation (2018) — 645 citations
- Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine (2023) — 542 citations
- Microsoft Research Asia’s Systems for WMT19 (2019) — 26 citations
- BioGPT: Generative Pre-trained Transformer for Biomedical Text Generation and Mining (2022) — 1,456 citations
- Lightspeech: Lightweight and Fast Text to Speech with Neural Architecture Search (2021) — 81 citations
- Neural Architecture Search with GBDT (2020) — 32 citations
- Semi-Supervised Neural Architecture Search (2020) — 104 citations