Xinya Du
Artificial Intelligence · 2017–2020
A researcher in the Science corpus: 5 papers; trusts 328 researchers and is trusted by 77 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 89 citations into it; #2,824 of 35,345 by trust here, #6,956 by citations
- Natural Language Processing — 83 citations into it; #800 of 5,688 by trust here, #1,735 by citations
- Computer Vision and Pattern Recognition — 35 citations into it; #4,487 of 21,575 by trust here, #7,421 by citations
- Evaluation & Benchmarks — 28 citations into it
- Model Architectures — 16 citations into it
- Information Retrieval & Search — 10 citations into it
- Representation & Self-Supervised Learning — 7 citations into it
- Information Systems — 6 citations into it
Whom Xinya Du trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Ilya Sutskever
- Sepp Hochreiter
- Geoffrey E. Hinton
- Dzmitry Bahdanau
- Christopher D. Manning
- Kyunghyun Cho
- J. Urgen Schmidhuber
- Alex Krizhevsky
- Diederik P. Kingma
Who trusts Xinya Du overall
- Shiyue Zhang
- Vishwajeet Kumar
- Tom Hosking
- Sebastian Riedel
- Patrick Lewis
- Wenhui Wang
- Sijia Wang
- Claire Cardie
- Mohit Bansal
- Qian Yu
Papers from Xinya Du's perspective
Ordered by how strongly the researchers Xinya Du trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Gradient-based learning applied to document recognition
- Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
Their papers
- Learning to Ask: Neural Question Generation for Reading Comprehension (2017) — 741 citations
- Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (2018) — 174 citations
- Be Consistent! Improving Procedural Text Comprehension using Label Consistency (2019) — 31 citations
- Identifying Where to Focus in Reading Comprehension for Neural Question Generation (2017) — 101 citations
- Event Extraction by Answering (Almost) Natural Questions (2020) — 492 citations