Aurelia Guy

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 9 papers; trusts 120 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 229 citations into it; #21,200 of 35,345 by trust here, #1,154 by citations
• Computer Vision and Pattern Recognition — 101 citations into it
• Reinforcement Learning — 88 citations into it; #1,682 of 2,242 by trust here, #1,280 by citations
• Natural Language Processing — 62 citations into it
• Optimization & Training — 42 citations into it; #2,012 of 2,601 by trust here, #1,335 by citations
• Evaluation & Benchmarks — 30 citations into it
• Control and Systems Engineering — 18 citations into it
• Generative Models — 18 citations into it

Whom Aurelia Guy trusts overall

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

1. John Schulman
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Richard S. Sutton
5. Sergey Levine
6. Yoshua Bengio
7. Alex Krizhevsky
8. Ilya Sutskever
9. Sham M. Kakade
10. Ronald J. Williams

Who trusts Aurelia Guy overall

1. Wen Sun
2. Jack Parker-Holder
3. Anirudh Vemula
4. Daochen Zha
5. Santiago Paternain
6. Mingyang Geng
7. Nathan H. Jay
8. Jacky Liang
9. Aldo Pacchiano
10. Sadhika Malladi

Papers from Aurelia Guy's perspective

Ordered by how strongly the researchers Aurelia Guy 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. Learning Multiple Layers of Features from Tiny Images
9. GENERATIVE ADVERSARIAL NETS
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
• Training Compute-Optimal Large Language Models (2022) — 3,535 citations
• Improving language models by retrieving from trillions of tokens (2021) — 1,785 citations
• Imitating Interactive Intelligence (2020) — 74 citations
• Simple random search provides a competitive approach to reinforcement learning (2018) — 338 citations
• Intra-agent speech permits zero-shot task acquisition (2022) — 10 citations
• Unified Scaling Laws for Routed Language Models (2022) — 285 citations
• An empirical analysis of compute-optimal large language model training (2022) — 516 citations
• Simple random search of static linear policies is competitive for reinforcement learning (2018) — 309 citations