Carolin (Haas) Lawrence

Artificial Intelligence · 2016–2020

A researcher in the Science corpus: 6 papers; trusts 198 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 84 citations into it; #9,557 of 35,345 by trust here, #16,166 by citations
• Natural Language Processing — 59 citations into it; #1,637 of 5,688 by trust here, #3,154 by citations
• Reinforcement Learning — 54 citations into it; #912 of 2,242 by trust here, #1,586 by citations
• Management Science and Operations Research — 33 citations into it; #2,907 of 11,946 by trust here, #4,602 by citations
• Computer Vision and Pattern Recognition — 15 citations into it; #16,026 of 21,575 by trust here, #20,271 by citations
• Evaluation & Benchmarks — 15 citations into it
• Statistics and Probability — 11 citations into it
• Optimization & Training — 5 citations into it

Whom Carolin (Haas) Lawrence trusts overall

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

1. Franz Joseph Och
2. Richard S. Sutton
3. Matthew D. Zeiler
4. Yoshua Bengio
5. Ronald J. Williams
6. Csaba Szepesvari
7. Geoffrey E. Hinton
8. Ilya Sutskever
9. Philipp Koehn
10. Stefan Riezler

Who trusts Carolin (Haas) Lawrence overall

1. Stefan Riezler
2. Julia Kreutzer
3. Shahram Khadivi
4. Sahisnu Mazumder
5. Zhuang Li
6. Marco Damonte
7. Pavel Petrushkov
8. Evgeny Matusov
9. Tianyu Wu
10. Lizhen Qu

Papers from Carolin (Haas) Lawrence's perspective

Ordered by how strongly the researchers Carolin (Haas) Lawrence trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
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. 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

• Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback (2018) — 58 citations
• A Corpus and Semantic Parser for Multilingual Natural Language Querying of OpenStreetMap (2016) — 35 citations
• Offline Reinforcement Learning from Human Feedback in Real-World Sequence-to-Sequence Tasks (2020) — 18 citations
• Counterfactual Learning from Bandit Feedback under Deterministic Logging : A Case Study in Statistical Machine Translation (2017) — 33 citations
• Counterfactual Learning for Machine Translation: Degeneracies and Solutions (2017) — 7 citations
• NLmaps: A Natural Language Interface to Query OpenStreetMap (2016) — 29 citations