Jiezhong Qiu
Artificial Intelligence · 2017–2021
A researcher in the Science corpus: 8 papers; trusts 270 researchers and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 430 citations into it; #20,237 of 35,345 by trust here, #3,440 by citations
- Computer Vision and Pattern Recognition — 211 citations into it
- Natural Language Processing — 175 citations into it; #5,654 of 5,688 by trust here, #3,053 by citations
- Large Language Models — 118 citations into it; #619 of 799 by trust here, #420 by citations
- Representation & Self-Supervised Learning — 88 citations into it; #2,740 of 3,145 by trust here, #1,181 by citations
- Model Architectures — 65 citations into it
- Statistical and Nonlinear Physics — 52 citations into it; #7,419 of 10,127 by trust here, #1,867 by citations
- Evaluation & Benchmarks — 47 citations into it
Whom Jiezhong Qiu trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Geoffrey E. Hinton
- Diederik P. Kingma
- Ilya Sutskever
- Alex Krizhevsky
- Kaiming He
- Yann LeCun
- Tomas Mikolov
- Sepp Hochreiter
- Jure Leskovec
Who trusts Jiezhong Qiu overall
- Benedek Rozemberczki
- Shirui Pan
- Xiang Wang
- Yanqiao Zhu
- Ankit Gupta
- Xiao Liu
- Yixin Liu
- Aleksandar Bojchevski
- Johannes Klicpera
- Yi Tay
Papers from Jiezhong Qiu's perspective
Ordered by how strongly the researchers Jiezhong Qiu 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Blockwise Self-Attention for Long Document Understanding (2019) — 280 citations
- Pre-Trained Models: Past, Present and Future (2021) — 1,095 citations
- GLM: General Language Model Pretraining with Autoregressive Blank Infilling (2021) — 1,956 citations
- Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec (2017) — 987 citations
- FastMoE: A Fast Mixture-of-Expert Training System (2021) — 148 citations
- All NLP Tasks Are Generation Tasks: A General Pretraining Framework (2021) — 68 citations
- GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training (2020) — 1,143 citations
- DeepInf: Social Influence Prediction with Deep Learning (2018) — 625 citations