Hui Su
Artificial Intelligence · with Shen · 2017–2021
A researcher in the Science corpus: 4 papers; trusts 143 researchers and is trusted by 8 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 113 citations into it; #17,641 of 35,345 by trust here, #7,112 by citations
- Natural Language Processing — 108 citations into it; #3,253 of 5,688 by trust here, #1,649 by citations
- Computer Vision and Pattern Recognition — 44 citations into it
- Evaluation & Benchmarks — 17 citations into it
- Model Architectures — 10 citations into it
- Generative Models — 5 citations into it
Whom Hui Su trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Diederik P. Kingma
- Yoshua Bengio
- Geoffrey E. Hinton
- Jiwei Li
- Ilya Sutskever
- Sepp Hochreiter
- Jimmy Ba
- Alex Krizhevsky
- Kyunghyun Cho
- Oriol Vinyals
Who trusts Hui Su overall
- Eric Michael Smith
- Pei Zhou
- Myle Ott
- Hannah Rashkin
- Sarik Ghazarian
- Deepanway Ghosal
- Chen Zhang
- Haolan Zhan
- Wangchunshu Zhou
- Baber Khalid
Papers from Hui Su's perspective
Ordered by how strongly the researchers Hui Su 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
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- 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
- DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset (2017) — 1,534 citations
- Neural Data-to-Text Generation via Jointly Learning the Segmentation and Correspondence (2020) — 51 citations
- Neural Data-to-Text Generation with LM-based Text Augmentation (2021) — 46 citations
- Diversifying Dialogue Generation with Non-Conversational Text (2020) — 47 citations