Daniel King

Artificial Intelligence · with Beltagy · 2019–2024

A researcher in the Science corpus: 3 papers; trusts 32 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 93 citations into it; #17,325 of 35,345 by trust here, #21,316 by citations
• Natural Language Processing — 53 citations into it; #3,049 of 5,688 by trust here, #4,361 by citations
• Evaluation & Benchmarks — 24 citations into it
• Molecular Biology — 20 citations into it; #6,287 of 17,773 by trust here, #13,284 by citations
• Model Architectures — 8 citations into it

Whom Daniel King trusts overall

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

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

Who trusts Daniel King overall

1. Kyle Lo
2. Iz Beltagy
3. Monica Agrawal
4. Arman Cohan
5. Lucy Lu Wang
6. Isabel Cachola
7. A. Levy
8. Gabriele Pergola

Papers from Daniel King's perspective

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

Their papers

• ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing (2019) — 870 citations
• Don’t Say What You Don’t Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search (2022) — 36 citations
• LoRA Learns Less and Forgets Less (2024) — 354 citations