Dan Friedman

Artificial Intelligence · 2021–2022

A researcher in the Science corpus: 2 papers; trusts 240 researchers and is trusted by 17 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #10,667 of 35,345 by trust here, #12,580 by citations
• Computer Vision and Pattern Recognition — 32 citations into it
• Natural Language Processing — 22 citations into it; #2,102 of 5,688 by trust here, #2,695 by citations
• Generative Models — 18 citations into it
• Evaluation & Benchmarks — 17 citations into it; #1,080 of 3,147 by trust here, #1,653 by citations
• Large Language Models — 15 citations into it
• Computational Theory and Mathematics — 5 citations into it
• Signal Processing — 5 citations into it

Whom Dan Friedman trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Tero Karras
6. Kaiming He
7. Prafulla Dhariwal
8. Alec Radford
9. Yann LeCun
10. Ilya Sutskever

Who trusts Dan Friedman overall

1. Boshi Wang
2. Hyojin Bahng
3. Ethan Perez
4. Xiang Deng
5. Ohad Rubin
6. Hao Huang
7. Ali Jahanian
8. A. Chevalier
9. Badr AlKhamissi
10. Yisheng Song

Papers from Dan Friedman's perspective

Ordered by how strongly the researchers Dan Friedman 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. ImageNet Large Scale Visual Recognition Challenge
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Factual Probing Is [MASK]: Learning vs. Learning to Recall (2021) — 460 citations
• The Vendi Score: A Diversity Evaluation Metric for Machine Learning (2022) — 306 citations