Luke Metz
Machine Learning, Robotics, Making
Publications
2019
Understanding and correcting pathologies in the training of learned optimizers
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein
ICML 2019 (20 min oral), NeurIPS 2018 metalearning workshop
Guided evolutionary strategies: Augmenting random search with surrogate gradients
Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein
ICML 2019
Meta-Learning Update Rules for Unsupervised Representation Learning
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein
ICLR 2019 (oral), COSYNE 2018 (oral), NeurIPS 2017 metalearning workshop
Towards GAN Benchmarks Which Require Generalization
Ishaan Gulrajani, Colin Raffel, Luke Metz
ICLR 2019
Using learned optimizers to make models robust to input noise
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk
ICML workshop: Uncertainty and Robustness in Deep Learning 2019
2018
Adversarial Spheres
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow
2017
Compositional Pattern Producing GAN
Luke Metz, Ishaan Gulrajani
NeurIPS 2017 creativity workshop
Discrete Sequential Prediction of Continuous Actions for Deep RL
Luke Metz, Julian Ibarz, Navdeep Jaitly, James Davidson
Began: Boundary Equilibrium Generative Adversarial Networks
David Berthelot, Tom Schumm, Luke Metz
Unrolled Generative Adversarial Networks
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein
ICLR 2017
2016
Unsupervised Representation Learning With Deep Convolutional Generative Adversarial Networks
Alec Radford, Luke Metz, Soumith Chintala
ICLR 2016