Jing Jin

Artificial Intelligence · with Bai · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 36 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it
• Computer Vision and Pattern Recognition — 46 citations into it
• Efficiency & Compression — 45 citations into it
• Model Architectures — 17 citations into it
• Natural Language Processing — 16 citations into it

Whom Jing Jin trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Ilya Sutskever
6. Oriol Vinyals
7. Raghuraman Krishnamoorthi
8. Yann LeCun
9. Kaiming He
10. J. Scott McCarley

Who trusts Jing Jin overall

1. Zi-Mei Yao
2. Sehoon Kim

Papers from Jing Jin's perspective

Ordered by how strongly the researchers Jing Jin 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• BinaryBERT: Pushing the Limit of BERT Quantization (2020) — 276 citations
• KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization (2021) — 30 citations