Jie Zhou

Artificial Intelligence · with Fang · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 17 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 25 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Efficiency & Compression — 16 citations into it
• Optimization & Training — 9 citations into it
• Large Language Models — 7 citations into it
• Natural Language Processing — 6 citations into it

Whom Jie Zhou trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Kaiming He
5. Ilya Sutskever
6. Sepp Hochreiter
7. Jacob Devlin
8. Alec Radford
9. Yoshua Bengio
10. Christian Szegedy

Papers from Jie Zhou's perspective

Ordered by how strongly the researchers Jie Zhou trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Going deeper with convolutions
8. Gradient-based learning applied to document recognition
9. ImageNet: A large-scale hierarchical image database
10. Language Models are Unsupervised Multitask Learners

Their papers

• TurboTransformers: an efficient GPU serving system for transformer models (2020) — 180 citations
• Parallel Training of Pre-Trained Models via Chunk-Based Dynamic Memory Management (2021) — 48 citations