Ting Hua

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 42 citations into it
• Natural Language Processing — 14 citations into it

Whom Ting Hua trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Richard Socher
5. Alex Krizhevsky
6. Jacob Devlin
7. Ilya Sutskever
8. Kaiming He
9. Yann LeCun
10. Wenhui Wang

Papers from Ting Hua's perspective

Ordered by how strongly the researchers Ting Hua 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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Language model compression with weighted low-rank factorization (2022) — 241 citations
• Automatic Mixed-Precision Quantization Search of BERT (2021) — 26 citations