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Luke Metz
Luke Metz
OpenAI
openai.com의 이메일 확인됨 - 홈페이지
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Unsupervised representation learning with deep convolutional generative adversarial networks
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
158912015
Began: Boundary equilibrium generative adversarial networks
D Berthelot, T Schumm, L Metz
arXiv preprint arXiv:1703.10717, 2017
13972017
Unrolled generative adversarial networks
L Metz, B Poole, D Pfau, J Sohl-Dickstein
arXiv preprint arXiv:1611.02163, 2016
10802016
Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
4822015
Adversarial spheres
J Gilmer, L Metz, F Faghri, SS Schoenholz, M Raghu, M Wattenberg, ...
arXiv preprint arXiv:1801.02774, 2018
3462018
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
3352022
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
1142019
Meta-learning update rules for unsupervised representation learning
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
arXiv preprint arXiv:1804.00222, 2018
1142018
Unsupervised representation learning with deep convolutional generative adversarial networks (2016)
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
1122015
Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv e-prints
A Radford, L Metz, S Chintala
arXiv preprint arXiv:1511.06434, 2015
1052015
Discrete sequential prediction of continuous actions for deep rl
L Metz, J Ibarz, N Jaitly, J Davidson
arXiv preprint arXiv:1705.05035, 2017
972017
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
852019
Towards GAN benchmarks which require generalization
I Gulrajani, C Raffel, L Metz
arXiv preprint arXiv:2001.03653, 2020
542020
Gradients are not all you need
L Metz, CD Freeman, SS Schoenholz, T Kachman
arXiv preprint arXiv:2111.05803, 2021
492021
On linear identifiability of learned representations
G Roeder, L Metz, D Kingma
International Conference on Machine Learning, 9030-9039, 2021
492021
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
442020
Learning an adaptive learning rate schedule
Z Xu, AM Dai, J Kemp, L Metz
arXiv preprint arXiv:1909.09712, 2019
442019
Learning unsupervised learning rules
L Metz, N Maheswaranathan, B Cheung, J Sohl-Dickstein
International Conference on Learning Representations, 2019
442019
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
402021
Learning to predict without looking ahead: World models without forward prediction
D Freeman, D Ha, L Metz
Advances in Neural Information Processing Systems 32, 2019
392019
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