Asa Cooper Stickland

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 4 papers; trusts 133 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 117 citations into it
• Natural Language Processing — 76 citations into it
• Computer Vision and Pattern Recognition — 61 citations into it
• Large Language Models — 34 citations into it
• Evaluation & Benchmarks — 21 citations into it
• Model Architectures — 16 citations into it
• Representation & Self-Supervised Learning — 7 citations into it
• Information Systems — 6 citations into it

Whom Asa Cooper Stickland trusts overall

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

1. Diederik P. Kingma
2. Rico Sennrich
3. Philipp Koehn
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Ilya Sutskever
7. Matt Post
8. Jimmy Ba
9. Alex Krizhevsky
10. Kaiming He

Who trusts Asa Cooper Stickland overall

1. Surangika Ranathunga
2. Xian Li
3. E. Lee
4. Changhan Wang
5. Xiang Lisa Li
6. Junliang Guo

Papers from Asa Cooper Stickland's perspective

Ordered by how strongly the researchers Asa Cooper Stickland 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. Attention is All you Need (Neural Information Processing Systems)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Gradient-based learning applied to document recognition
10. ImageNet: A large-scale hierarchical image database

Their papers

• Deep Transformers with Latent Depth (2020) — 31 citations
• Recipes for Adapting Pre-trained Monolingual and Multilingual Models to Machine Translation (2020) — 51 citations
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark (2023) — 2,996 citations
• The Reversal Curse: LLMs trained on "A is B" fail to learn "B is A" (2023) — 511 citations