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Catherine Olsson
Catherine Olsson
Anthropic
Verified email at mit.edu
Title
Cited by
Cited by
Year
Estimating the reproducibility of psychological science
Open Science Collaboration
Science 349 (6251), aac4716, 2015
71572015
Dota 2 with large scale deep reinforcement learning
C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 2019
9592019
An open, large-scale, collaborative effort to estimate the reproducibility of psychological science
Open Science Collaboration
Perspectives on Psychological Science 7, 657-660, 2012
6592012
Tensorfuzz: Debugging neural networks with coverage-guided fuzzing
A Odena, C Olsson, D Andersen, I Goodfellow
International Conference on Machine Learning, 4901-4911, 2019
2302019
Is generator conditioning causally related to GAN performance?
A Odena, J Buckman, C Olsson, T Brown, C Olah, C Raffel, I Goodfellow
International conference on machine learning, 3849-3858, 2018
1172018
Discriminator rejection sampling
S Azadi, C Olsson, T Darrell, I Goodfellow, A Odena
arXiv preprint arXiv:1810.06758, 2018
1112018
Unrestricted adversarial examples
TB Brown, N Carlini, C Zhang, C Olsson, P Christiano, I Goodfellow
arXiv preprint arXiv:1809.08352, 2018
712018
Dota 2 with large scale deep reinforcement learning
CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680 2, 2019
692019
Predicting actions from static scenes
TH Vu, C Olsson, I Laptev, A Oliva, J Sivic
European Conference on Computer Vision, 421-436, 2014
682014
Skill rating for generative models
C Olsson, S Bhupatiraju, T Brown, A Odena, I Goodfellow
arXiv preprint arXiv:1808.04888, 2018
372018
The Reproducibility Project: A model of large-scale collaboration for empirical research on reproducibility
HB Kappes, Open Science Collaboration
CRC Press, Taylor & Francis Group, 2014
372014
Dota 2 with large scale deep reinforcement learning. arXiv 2019
C Berner, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
arXiv preprint arXiv:1912.06680, 0
23
Dota 2 with large scale deep reinforcement learning. 2019
CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ...
URL https://arxiv. org/abs, 2019
18*2019
Dawn Drain
N Elhage, N Nanda, C Olsson, T Henighan, N Joseph, B Mann, A Askell, ...
Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Andy Jones, Jackson …, 2021
162021
Finding and Explaining Similarities in Linked Data.
C Olsson, P Petrov, J Sherman, A Perez-Lopez
STIDS, 52-59, 2011
162011
Predictability and surprise in large generative models
D Ganguli, D Hernandez, L Lovitt, A Askell, Y Bai, A Chen, T Conerly, ...
2022 ACM Conference on Fairness, Accountability, and Transparency, 1747-1764, 2022
152022
A general language assistant as a laboratory for alignment
A Askell, Y Bai, A Chen, D Drain, D Ganguli, T Henighan, A Jones, ...
arXiv preprint arXiv:2112.00861, 2021
122021
In-context learning and induction heads
C Olsson, N Elhage, N Nanda, N Joseph, N DasSarma, T Henighan, ...
arXiv preprint arXiv:2209.11895, 2022
32022
Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback
Y Bai, A Jones, K Ndousse, A Askell, A Chen, N DasSarma, D Drain, ...
arXiv preprint arXiv:2204.05862, 2022
32022
Scaling Laws and Interpretability of Learning from Repeated Data
D Hernandez, T Brown, T Conerly, N DasSarma, D Drain, S El-Showk, ...
arXiv preprint arXiv:2205.10487, 2022
12022
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