Weihua Luo
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 211 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 132 citations into it; #25,172 of 35,345 by trust here, #25,040 by citations
- Natural Language Processing — 127 citations into it; #3,577 of 5,688 by trust here, #4,032 by citations
- Computer Vision and Pattern Recognition — 69 citations into it
- Model Architectures — 21 citations into it
- Evaluation & Benchmarks — 17 citations into it
- Large Language Models — 17 citations into it
- Optimization & Training — 6 citations into it
Whom Weihua Luo trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Ilya Sutskever
- Geoffrey E. Hinton
- Rico Sennrich
- Oriol Vinyals
- Kyunghyun Cho
- Dzmitry Bahdanau
- Jimmy Ba
- Alex Krizhevsky
Who trusts Weihua Luo overall
Papers from Weihua Luo's perspective
Ordered by how strongly the researchers Weihua Luo trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet classification with deep convolutional neural networks
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- ImageNet: A large-scale hierarchical image database
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
Their papers
- Zero-Shot Cross-Lingual Abstractive Sentence Summarization through Teaching Generation and Attention (2019) — 69 citations
- On Learning Universal Representations Across Languages (2020) — 93 citations
- Towards Enhancing Faithfulness for Neural Machine Translation (2020) — 25 citations
- Code-Switching for Enhancing NMT with Pre-Specified Translation (2019) — 134 citations
- Modeling Coherence for Neural Machine Translation with Dynamic and Topic Caches (2017) — 101 citations