Jan Buys

Artificial Intelligence · 2015–2019

A researcher in the Science corpus: 6 papers; trusts 259 researchers and is trusted by 247 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 146 citations into it; #1,265 of 35,345 by trust here, #1,590 by citations
• Natural Language Processing — 138 citations into it; #509 of 5,688 by trust here, #408 by citations
• Computer Vision and Pattern Recognition — 52 citations into it
• Model Architectures — 33 citations into it; #1,059 of 2,278 by trust here, #1,665 by citations
• Evaluation & Benchmarks — 18 citations into it
• Molecular Biology — 9 citations into it
• Optimization & Training — 9 citations into it
• Large Language Models — 8 citations into it

Whom Jan Buys trusts overall

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

1. Christopher D. Manning
2. Joakim Nivre
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Daniel Klein
6. Ilya Sutskever
7. Alex Graves
8. Eugene Charniak
9. Diederik P. Kingma
10. Michael Collins

Who trusts Jan Buys overall

1. Timo Schick
2. Rowan Zellers
3. Kalpesh Krishna
4. Yejin Choi
5. Daniel Fried
6. Sean Welleck
7. Kyunghyun Cho
8. Ximing Lu
9. Chris Dyer
10. Alisa Liu

Papers from Jan Buys's perspective

Ordered by how strongly the researchers Jan Buys 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• The Curious Case of Neural Text Degeneration (2019) — 4,380 citations
• Learning to Write with Cooperative Discriminators (2018) — 256 citations
• Online Segment to Segment Neural Transduction (2016) — 82 citations
• Generative Incremental Dependency Parsing with Neural Networks (2015) — 19 citations
• Benchmarking Hierarchical Script Knowledge (2019) — 11 citations
• A Bayesian Model for Generative Transition-based Dependency Parsing (2015) — 10 citations