Luke Metz

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

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