Xiaoxia Wu

Artificial Intelligence · with He · 2022

A researcher in the Science corpus: 2 papers; trusts 56 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it; #22,072 of 35,345 by trust here, #15,236 by citations
• Efficiency & Compression — 66 citations into it; #771 of 1,264 by trust here, #804 by citations
• Computer Vision and Pattern Recognition — 61 citations into it; #13,699 of 21,575 by trust here, #10,334 by citations
• Natural Language Processing — 38 citations into it
• Large Language Models — 37 citations into it
• Evaluation & Benchmarks — 20 citations into it
• Model Architectures — 17 citations into it

Whom Xiaoxia Wu trusts overall

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

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

Who trusts Xiaoxia Wu overall

1. Elias Frantar
2. Sehoon Kim
3. Karttikeya Mangalam

Papers from Xiaoxia Wu's perspective

Ordered by how strongly the researchers Xiaoxia Wu 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. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers (2022) — 759 citations
• Extreme Compression for Pre-trained Transformers Made Simple and Efficient (2022) — 31 citations