D. Gillick

Artificial Intelligence · 2009–2021

A researcher in the Science corpus: 9 papers; trusts 98 researchers and is trusted by 86 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 132 citations into it; #3,494 of 35,345 by trust here, #5,638 by citations
• Natural Language Processing — 109 citations into it; #1,572 of 5,688 by trust here, #1,826 by citations
• Computer Vision and Pattern Recognition — 39 citations into it; #7,475 of 21,575 by trust here, #5,843 by citations
• Representation & Self-Supervised Learning — 30 citations into it
• Evaluation & Benchmarks — 22 citations into it; #1,114 of 3,147 by trust here, #2,482 by citations
• Information Retrieval & Search — 16 citations into it; #468 of 1,022 by trust here, #438 by citations
• Information Systems — 11 citations into it
• Management Science and Operations Research — 8 citations into it; #8,331 of 11,946 by trust here, #8,469 by citations

Whom D. Gillick trusts overall

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

1. Chin-Yew Lin
2. Ani Nenkova
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Yann LeCun
6. Ilya Sutskever
7. L. Eon Bottou
8. Diederik P. Kingma
9. Alex Krizhevsky
10. Alex Graves

Who trusts D. Gillick overall

1. Taylor Berg-Kirkpatrick
2. Greg Durrett
3. Maxime Peyrard
4. Yen-Chun Chen
5. Natalie Schluter
6. Daniel Klein
7. Fei Liu
8. Mohit Bansal
9. Annie Louis
10. Gautier Izacard

Papers from D. Gillick's perspective

Ordered by how strongly the researchers D. Gillick 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. Long Short-Term Memory
4. Attention is All you Need (Neural Information Processing Systems)
5. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Multilingual Language Processing From Bytes (2015) — 229 citations
• Time-Aware Language Models as Temporal Knowledge Bases (2021) — 394 citations
• End-to-End Retrieval in Continuous Space (2018) — 108 citations
• A Scalable Global Model for Summarization (2009) — 311 citations
• Learning Dense Representations for Entity Retrieval (2019) — 230 citations
• Embedding Methods for Fine Grained Entity Type Classification (2015) — 126 citations
• A Fast, Compact, Accurate Model for Language Identification of Codemixed Text (2018) — 36 citations
• Jointly Learning to Extract and Compress (2011) — 238 citations
• Non-Expert Evaluation of Summarization Systems is Risky (2010) — 104 citations