Chris van der Lee
Artificial Intelligence · 2019–2021
A researcher in the Science corpus: 4 papers; trusts 36 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 99 citations into it; #10,407 of 35,345 by trust here, #10,065 by citations
- Natural Language Processing — 86 citations into it; #2,046 of 5,688 by trust here, #2,220 by citations
- Evaluation & Benchmarks — 73 citations into it; #1,303 of 3,147 by trust here, #1,383 by citations
- Statistics and Probability — 29 citations into it
- Natural Language Generation — 6 citations into it; #173 of 199 by trust here, #85 by citations
Whom Chris van der Lee trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Ehud Reiter
- Philipp Koehn
- Jekaterina Novikova
- Ondrej Dusek
- Geoff Cumming
- Matthew G. Snover
- Dimitra Gkatzia
- Emily Pitler
- Yoshua Bengio
- Sepp Hochreiter
Who trusts Chris van der Lee overall
- Anastasia Shimorina
- Elizabeth Clark
- Anya Belz
- Ehud Reiter
- Sebastian Gehrmann
- Craig Thomson
- David M. Howcroft
- Ratish Puduppully
- Mihir Kale
- Neslihan Iskender
Papers from Chris van der Lee's perspective
Ordered by how strongly the researchers Chris van der Lee trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Long Short-Term Memory
- 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
- BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
- 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)
- Bleu: a Method for Automatic Evaluation of Machine Translation
- Gradient-based learning applied to document recognition
Their papers
- Best practices for the human evaluation of automatically generated text (2019) — 276 citations
- Neural data-to-text generation: A comparison between pipeline and end-to-end architectures (2019) — 151 citations
- Human evaluation of automatically generated text: Current trends and best practice guidelines (2021) — 216 citations
- The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) (2020)