Catherine Olsson

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 18 papers; trusts 294 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 445 citations into it; #13,958 of 35,345 by trust here, #1,063 by citations
• Computer Vision and Pattern Recognition — 184 citations into it; #9,843 of 21,575 by trust here, #2,552 by citations
• Evaluation & Benchmarks — 77 citations into it; #2,777 of 3,147 by trust here, #1,817 by citations
• Generative Models — 67 citations into it; #1,010 of 1,956 by trust here, #961 by citations
• Representation & Self-Supervised Learning — 23 citations into it
• Signal Processing — 19 citations into it
• Probabilistic & Bayesian Methods — 15 citations into it; #1,010 of 1,525 by trust here, #817 by citations
• Media Technology — 9 citations into it

Whom Catherine Olsson trusts overall

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

1. Ian J. Goodfellow
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Diederik P. Kingma
5. Yoshua Bengio
6. Kaiming He
7. Yann LeCun
8. Christian Szegedy
9. Tim Salimans
10. David G. Lowe

Who trusts Catherine Olsson overall

1. Ali Borji
2. Wangchunshu Zhou
3. Michael Arbel
4. Tatsunori B. Hashimoto
5. Jiaming Song
6. Paulina Grnarova
7. Augustus Odena
8. Amanda Rios
9. Neel Nanda
10. Arthur Conmy

Papers from Catherine Olsson's perspective

Ordered by how strongly the researchers Catherine Olsson 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
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

• Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback (2022) — 4,254 citations
• Dota 2 with Large Scale Deep Reinforcement Learning (2019) — 2,179 citations
• Constitutional AI: Harmlessness from AI Feedback (2022) — 3,377 citations
• Discriminator Rejection Sampling (2018) — 144 citations
• Is Generator Conditioning Causally Related to GAN Performance? (2018) — 124 citations
• Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned (2022) — 837 citations
• Predictability and Surprise in Large Generative Models (2022) — 365 citations
• In-context Learning and Induction Heads (2022) — 942 citations
• Language Models (Mostly) Know What They Know (2022) — 1,782 citations
• Skill Rating for Generative Models (2018) — 35 citations
• Discovering Language Model Behaviors with Model-Written Evaluations (2022) — 917 citations
• The Capacity for Moral Self-Correction in Large Language Models (2023) — 214 citations
• Unrestricted Adversarial Examples (2018) — 108 citations
• Predicting Actions from Static Scenes (2014) — 66 citations
• Toy Models of Superposition (2022) — 947 citations
• Estimating the reproducibility of psychological science (2015) — 7,174 citations
• Scaling Laws and Interpretability of Learning from Repeated Data (2022) — 166 citations
• A General Language Assistant as a Laboratory for Alignment (2021) — 1,175 citations