Get our free extension to see links to code for papers anywhere online!

 Add to Chrome

 Add to Firefox

CatalyzeX Code Finder - Browser extension linking code for ML papers across the web! | Product Hunt Embed
Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian

Nov 12, 2020
Jack Parker-Holder, Luke Metz, Cinjon Resnick, Hengyuan Hu, Adam Lerer, Alistair Letcher, Alex Peysakhovich, Aldo Pacchiano, Jakob Foerster

* Camera-ready version, NeurIPS 2020 

  Access Paper or Ask Questions

Reverse engineering learned optimizers reveals known and novel mechanisms

Nov 04, 2020
Niru Maheswaranathan, David Sussillo, Luke Metz, Ruoxi Sun, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

Tasks, stability, architecture, and compute: Training more effective learned optimizers, and using them to train themselves

Sep 23, 2020
Luke Metz, Niru Maheswaranathan, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

On Linear Identifiability of Learned Representations

Jul 08, 2020
Geoffrey Roeder, Luke Metz, Diederik P. Kingma


  Access Paper or Ask Questions

Using a thousand optimization tasks to learn hyperparameter search strategies

Mar 11, 2020
Luke Metz, Niru Maheswaranathan, Ruoxi Sun, C. Daniel Freeman, Ben Poole, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

Towards GAN Benchmarks Which Require Generalization

Jan 10, 2020
Ishaan Gulrajani, Colin Raffel, Luke Metz

* ICLR 2019 conference paper 

  Access Paper or Ask Questions

Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Oct 31, 2019
C. Daniel Freeman, Luke Metz, David Ha

* To appear at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019) 

  Access Paper or Ask Questions

Learning an Adaptive Learning Rate Schedule

Sep 20, 2019
Zhen Xu, Andrew M. Dai, Jonas Kemp, Luke Metz


  Access Paper or Ask Questions

Using learned optimizers to make models robust to input noise

Jun 08, 2019
Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk


  Access Paper or Ask Questions

Learned optimizers that outperform SGD on wall-clock and test loss

Oct 26, 2018
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, C. Daniel Freeman, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

Adversarial Spheres

Sep 10, 2018
Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S. Schoenholz, Maithra Raghu, Martin Wattenberg, Ian Goodfellow


  Access Paper or Ask Questions

Guided evolutionary strategies: escaping the curse of dimensionality in random search

Jun 28, 2018
Niru Maheswaranathan, Luke Metz, George Tucker, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

Discrete Sequential Prediction of Continuous Actions for Deep RL

Jun 16, 2018
Luke Metz, Julian Ibarz, Navdeep Jaitly, James Davidson


  Access Paper or Ask Questions

Learning Unsupervised Learning Rules

May 23, 2018
Luke Metz, Niru Maheswaranathan, Brian Cheung, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

BEGAN: Boundary Equilibrium Generative Adversarial Networks

May 31, 2017
David Berthelot, Thomas Schumm, Luke Metz


  Access Paper or Ask Questions

Unrolled Generative Adversarial Networks

May 12, 2017
Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein


  Access Paper or Ask Questions

Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Jan 07, 2016
Alec Radford, Luke Metz, Soumith Chintala

* Under review as a conference paper at ICLR 2016 

  Access Paper or Ask Questions