Rui Wang

Artificial Intelligence · 10 papers · 2018–2023

A researcher in the Science corpus: 10 papers; trusts 640 researchers and is trusted by 10 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 307 citations into it; #13,669 of 35,345 by trust here, #12,974 by citations
• Natural Language Processing — 253 citations into it; #2,544 of 5,688 by trust here, #2,767 by citations
• Computer Vision and Pattern Recognition — 170 citations into it; #10,374 of 21,575 by trust here, #10,677 by citations
• Model Architectures — 51 citations into it
• Evaluation & Benchmarks — 44 citations into it
• Multimodal Learning — 37 citations into it
• Representation & Self-Supervised Learning — 27 citations into it
• Large Language Models — 21 citations into it

Whom Rui Wang trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Kyunghyun Cho
8. Rico Sennrich
9. Dzmitry Bahdanau
10. Jimmy Ba

Who trusts Rui Wang overall

1. Roee Aharoni
2. Garrett Wilson
3. Surangika Ranathunga
4. Zhuosheng Zhang
5. Yoav Goldberg
6. Diane Joyce Cook

Papers from Rui Wang's perspective

Ordered by how strongly the researchers Rui Wang 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization (2019) — 60 citations
• SG-Net: Syntax-Guided Machine Reading Comprehension (2019) — 195 citations
• Syntax-Aware Data Augmentation for Neural Machine Translation (2020) — 23 citations
• A Survey of Domain Adaptation for Neural Machine Translation (2018) — 292 citations
• Universal Multimodal Representation for Language Understanding (2023) — 35 citations
• Data-dependent Gaussian Prior Objective for Language Generation (2020) — 69 citations
• Neural Machine Translation with Universal Visual Representation (2020) — 122 citations
• Relational Graph Attention Network for Aspect-based Sentiment Analysis (2020) — 698 citations
• Lattice-Based Transformer Encoder for Neural Machine Translation (2019) — 45 citations
• NICT’s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task (2019) — 16 citations