Conglong Li

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 5 papers; trusts 107 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 276 citations into it; #21,928 of 35,345 by trust here, #12,083 by citations
• Natural Language Processing — 127 citations into it
• Computer Vision and Pattern Recognition — 121 citations into it; #13,700 of 21,575 by trust here, #10,010 by citations
• Large Language Models — 107 citations into it
• Efficiency & Compression — 106 citations into it; #767 of 1,264 by trust here, #672 by citations
• Evaluation & Benchmarks — 65 citations into it
• Model Architectures — 37 citations into it
• Optimization & Training — 31 citations into it

Whom Conglong Li trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Alex Krizhevsky
5. Kaiming He
6. Ilya Sutskever
7. Yann LeCun
8. Sepp Hochreiter
9. Jimmy Ba
10. Alec Radford

Who trusts Conglong Li overall

1. Elias Frantar

Papers from Conglong Li's perspective

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

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
9. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale (2022) — 591 citations
• ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers (2022) — 759 citations
• 1-bit Adam: Communication Efficient Large-Scale Training with Adam's Convergence Speed (2021) — 111 citations
• Extreme Compression for Pre-trained Transformers Made Simple and Efficient (2022) — 31 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations