Jiawei Wu

Computer Vision and Pattern Recognition · 2017–2019

A researcher in the Science corpus: 5 papers; trusts 492 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 134 citations into it; #14,738 of 21,575 by trust here, #6,281 by citations
• Artificial Intelligence — 125 citations into it
• Natural Language Processing — 99 citations into it
• Multimodal Learning — 53 citations into it
• Evaluation & Benchmarks — 39 citations into it
• Model Architectures — 23 citations into it
• Reinforcement Learning — 12 citations into it; #2,023 of 2,242 by trust here, #1,663 by citations
• Optimization & Training — 10 citations into it

Whom Jiawei Wu trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Sepp Hochreiter
7. Kyunghyun Cho
8. Kaiming He
9. Yann LeCun
10. Dzmitry Bahdanau

Who trusts Jiawei Wu overall

1. Di Jin
2. Surangika Ranathunga
3. Bill Yuchen Lin
4. Linjie Li
5. Felix Stahlberg
6. Ziqi Zhang
7. Jie Lei
8. Xin Eric Wang
9. Yi Tay
10. Antoine Miech

Papers from Jiawei Wu's perspective

Ordered by how strongly the researchers Jiawei 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• VaTeX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research (2019) — 718 citations
• Video Captioning via Hierarchical Reinforcement Learning (2017) — 254 citations
• Learning to Compose Topic-Aware Mixture of Experts for Zero-Shot Video Captioning (2018) — 41 citations
• TWEETQA: A Social Media Focused Question Answering Dataset (2019) — 87 citations
• Extract and Edit: An Alternative to Back-Translation for Unsupervised Neural Machine Translation (2019) — 41 citations