Aidan Clark

Artificial Intelligence · 2019–2022

A researcher in the Science corpus: 12 papers; trusts 496 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 284 citations into it
• Computer Vision and Pattern Recognition — 224 citations into it; #8,417 of 21,575 by trust here, #4,690 by citations
• Generative Models — 115 citations into it; #839 of 1,956 by trust here, #540 by citations
• Optimization & Training — 74 citations into it
• Model Architectures — 67 citations into it
• Natural Language Processing — 65 citations into it
• Reinforcement Learning — 59 citations into it
• Evaluation & Benchmarks — 40 citations into it

Whom Aidan Clark trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Ilya Sutskever
5. Alex Krizhevsky
6. Kaiming He
7. Jimmy Ba
8. Yann LeCun
9. Christian Szegedy
10. Karen Simonyan

Who trusts Aidan Clark overall

1. Jacob Walker
2. Zhangyang Gao
3. Abdul Jabbar
4. Songwei Ge
5. Charlie Nash
6. Wenyi Hong
7. Aliaksandr Siarohin
8. Michael Dorkenwald
9. Daquan Zhou
10. Jeff Donahue

Papers from Aidan Clark's perspective

Ordered by how strongly the researchers Aidan Clark 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

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
• V-MPO: On-Policy Maximum a Posteriori Policy Optimization for Discrete and Continuous Control (2019) — 141 citations
• Adversarial Video Generation on Complex Datasets (2019) — 282 citations
• Transformation-based Adversarial Video Prediction on Large-Scale Data (2020) — 95 citations
• Stabilizing Transformers for Reinforcement Learning (2019) — 486 citations
• High Fidelity Speech Synthesis with Adversarial Networks (2019) — 264 citations
• TF-Replicator: Distributed Machine Learning for Researchers (2019) — 20 citations
• Unified Scaling Laws for Routed Language Models (2022) — 285 citations
• An empirical analysis of compute-optimal large language model training (2022) — 516 citations
• Skilful precipitation nowcasting using deep generative models of radar (2021) — 1,048 citations