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Luke Metz
Luke Metz
OpenAI
Verified email at openai.com - Homepage
Title
Cited by
Cited by
Year
Unsupervised representation learning with deep convolutional generative adversarial networks
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
177362015
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
arXiv preprint arXiv:1611.02163, 2016
38802016
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
16522023
Began: Boundary equilibrium generative adversarial networks
D Berthelot, T Schumm, L Metz
arXiv preprint arXiv:1703.10717, 2017
14942017
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
8242022
Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
5712015
Chatgpt: Optimizing language models for dialogue
J Schulman, B Zoph, C Kim, J Hilton, J Menick, J Weng, JFC Uribe, ...
OpenAI blog 2, 4, 2022
4062022
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
3862018
Understanding and correcting pathologies in the training of learned optimizers
L Metz, N Maheswaranathan, J Nixon, D Freeman, J Sohl-Dickstein
International Conference on Machine Learning, 4556-4565, 2019
1412019
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
1312018
Discrete sequential prediction of continuous actions for deep rl
L Metz, J Ibarz, N Jaitly, J Davidson
arXiv preprint arXiv:1705.05035, 2017
1142017
Guided evolutionary strategies: Augmenting random search with surrogate gradients
N Maheswaranathan, L Metz, G Tucker, D Choi, J Sohl-Dickstein
International Conference on Machine Learning, 4264-4273, 2019
972019
Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv e-prints
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434 1511, 2015
972015
Gradients are not all you need
L Metz, CD Freeman, SS Schoenholz, T Kachman
arXiv preprint arXiv:2111.05803, 2021
752021
On linear identifiability of learned representations
G Roeder, L Metz, D Kingma
International Conference on Machine Learning, 9030-9039, 2021
712021
Learning an adaptive learning rate schedule
Z Xu, AM Dai, J Kemp, L Metz
arXiv preprint arXiv:1909.09712, 2019
632019
Towards GAN benchmarks which require generalization
I Gulrajani, C Raffel, L Metz
arXiv preprint arXiv:2001.03653, 2020
592020
Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves
L Metz, N Maheswaranathan, CD Freeman, B Poole, J Sohl-Dickstein
arXiv preprint arXiv:2009.11243, 2020
562020
General-purpose in-context learning by meta-learning transformers
L Kirsch, J Harrison, J Sohl-Dickstein, L Metz
arXiv preprint arXiv:2212.04458, 2022
542022
Unbiased gradient estimation in unrolled computation graphs with persistent evolution strategies
P Vicol, L Metz, J Sohl-Dickstein
International Conference on Machine Learning, 10553-10563, 2021
532021
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