Nicholas FitzGerald

Artificial Intelligence · 2012–2024

A researcher in the Science corpus: 8 papers; trusts 186 researchers and is trusted by 67 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 149 citations into it; #3,749 of 35,345 by trust here, #3,907 by citations
• Natural Language Processing — 117 citations into it; #1,329 of 5,688 by trust here, #1,056 by citations
• Computer Vision and Pattern Recognition — 62 citations into it; #7,128 of 21,575 by trust here, #4,781 by citations
• Representation & Self-Supervised Learning — 33 citations into it
• Information Retrieval & Search — 19 citations into it
• Large Language Models — 18 citations into it
• Model Architectures — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it

Whom Nicholas FitzGerald trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Christopher D. Manning
6. Alex Krizhevsky
7. Yann LeCun
8. Richard Socher
9. Andriy Mnih
10. Matthew D. Zeiler

Who trusts Nicholas FitzGerald overall

1. Luke S. Zettlemoyer
2. Luheng He
3. Mark Yatskar
4. Kenton C. T. Lee
5. Bishan Yang
6. Swabha Swayamdipta
7. Diego Marcheggiani
8. Cynthia Matuszek
9. Angel Daza
10. Michael Roth

Papers from Nicholas FitzGerald's perspective

Ordered by how strongly the researchers Nicholas FitzGerald 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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• A Joint Model of Language and Perception for Grounded Attribute Learning (2012) — 323 citations
• Entities as Experts: Sparse Memory Access with Entity Supervision (2020) — 177 citations
• Semantic Role Labeling with Neural Network Factors (2015) — 152 citations
• Large-Scale QA-SRL Parsing (2018) — 106 citations
• Matching the Blanks: Distributional Similarity for Relation Learning (2019) — 866 citations
• Learning Cross-Context Entity Representations from Text (2020) — 33 citations
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context (2024) — 3,818 citations
• FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference (2022) — 44 citations