Antonia Creswell

Artificial Intelligence · 2016–2022

A researcher in the Science corpus: 8 papers; trusts 368 researchers and is trusted by 69 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 236 citations into it; #10,175 of 35,345 by trust here, #2,050 by citations
• Computer Vision and Pattern Recognition — 116 citations into it; #3,565 of 21,575 by trust here, #3,848 by citations
• Natural Language Processing — 93 citations into it; #2,236 of 5,688 by trust here, #1,546 by citations
• Large Language Models — 68 citations into it
• Prompting & Reasoning — 68 citations into it; #77 of 359 by trust here, #176 by citations
• Generative Models — 57 citations into it; #294 of 1,956 by trust here, #540 by citations
• Safety, Robustness & Interpretability — 24 citations into it
• Evaluation & Benchmarks — 21 citations into it

Whom Antonia Creswell trusts overall

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

1. Geoffrey E. Hinton
2. Alex Krizhevsky
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Graves
7. Yann LeCun
8. Kaiming He
9. Christian Szegedy
10. L. Eon Bottou

Who trusts Antonia Creswell overall

1. Yuval Alaluf
2. Jie Huang
3. Yotam Nitzan
4. Rameen Abdal
5. Yujun Shen
6. Jiapeng Zhu
7. Omer Tov
8. Michael Albright
9. Kevin Chen-Chuan Chang
10. S. Reddy

Papers from Antonia Creswell's perspective

Ordered by how strongly the researchers Antonia Creswell 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Scaling Language Models: Methods, Analysis & Insights from Training Gopher (2021) — 1,640 citations
• Can language models learn from explanations in context? (2022) — 383 citations
• Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning (2022) — 482 citations
• Inverting the Generator of a Generative Adversarial Network (2016) — 369 citations
• Faithful Reasoning Using Large Language Models (2022) — 163 citations
• Generative Adversarial Networks: An Overview (2017) — 3,805 citations
• Solving math word problems with process- and outcome-based feedback (2022) — 815 citations
• Language models show human-like content effects on reasoning (2022) — 230 citations