Yanqiao Zhu
Artificial Intelligence · 2018–2023
A researcher in the Science corpus: 6 papers; trusts 228 researchers and is trusted by 2 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 176 citations into it; #32,235 of 35,345 by trust here, #11,327 by citations
- Computer Vision and Pattern Recognition — 78 citations into it
- Information Systems — 67 citations into it; #7,701 of 8,771 by trust here, #1,707 by citations
- Representation & Self-Supervised Learning — 57 citations into it
- Natural Language Processing — 51 citations into it
- Graph Learning — 44 citations into it
- Evaluation & Benchmarks — 32 citations into it
- Large Language Models — 29 citations into it
Whom Yanqiao Zhu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Geoffrey E. Hinton
- Diederik P. Kingma
- Yoshua Bengio
- Alex Krizhevsky
- Ilya Sutskever
- Kaiming He
- Max Welling
- Thomas Kipf
- Yann LeCun
- Jimmy Ba
Who trusts Yanqiao Zhu overall
- Lirong Wu
- Yaochen Xie
- Xuheng Cai
- Zihan Lin
- Namkyeong Lee
- Xin Zhou
- Shantanu Thakoor
- Hengrui Zhang
- Zhao Xu
- Zhangyang Gao
Papers from Yanqiao Zhu's perspective
Ordered by how strongly the researchers Yanqiao Zhu 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)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models (2023) — 252 citations
- A Survey on Pretrained Language Models for Neural Code Intelligence (2022) — 19 citations
- Mining Latent Structures for Multimedia Recommendation (2021) — 428 citations
- Graph Contrastive Learning with Adaptive Augmentation (2020) — 1,475 citations
- Deep Graph Contrastive Representation Learning (2020) — 1,103 citations
- Session-based Recommendation with Graph Neural Networks (2018) — 1,914 citations