Lei Yu

Artificial Intelligence · with Blunsom · 2014–2019

A researcher in the Science corpus: 9 papers; trusts 358 researchers and is trusted by 126 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 243 citations into it; #2,111 of 35,345 by trust here, #3,175 by citations
• Natural Language Processing — 197 citations into it; #822 of 5,688 by trust here, #904 by citations
• Computer Vision and Pattern Recognition — 103 citations into it; #4,845 of 21,575 by trust here, #4,555 by citations
• Model Architectures — 50 citations into it; #1,058 of 2,278 by trust here, #1,665 by citations
• Large Language Models — 34 citations into it
• Representation & Self-Supervised Learning — 31 citations into it
• Evaluation & Benchmarks — 25 citations into it
• Probabilistic & Bayesian Methods — 23 citations into it; #435 of 1,525 by trust here, #521 by citations

Whom Lei Yu trusts overall

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

1. Diederik P. Kingma
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Alex Graves
5. Ilya Sutskever
6. Alex Krizhevsky
7. Jimmy Ba
8. Ruslan Salakhutdinov
9. Kyunghyun Cho
10. Yann LeCun

Who trusts Lei Yu overall

1. Aliaksei Severyn
2. Wenpeng Yin
3. Colin Raffel
4. Zhiguo Wang
5. Yusuke Yasuda
6. Yonatan Bisk
7. Yishu Miao
8. Alessandro Moschitti
9. Ryan J. Lowe
10. Dinghan Shen

Papers from Lei Yu's perspective

Ordered by how strongly the researchers Lei Yu 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

• Neural Variational Inference for Text Processing (2015) — 679 citations
• Learning and Evaluating General Linguistic Intelligence (2019) — 219 citations
• Deep Learning for Answer Sentence Selection (2014) — 421 citations
• Online Segment to Segment Neural Transduction (2016) — 82 citations
• The Neural Noisy Channel (2016) — 70 citations
• Unsupervised Recurrent Neural Network Grammars (2019) — 123 citations
• A Mutual Information Maximization Perspective of Language Representation Learning (2019) — 181 citations
• Putting Machine Translation in Context with the Noisy Channel Model (2019) — 7 citations
• Better Document-Level Machine Translation with Bayes’ Rule (2019) — 48 citations