When less is more: Simplifying inputs aids neural network understanding
Robin Tibor Schirrmeister, Rosanne Liu, Sara Hooker, Tonio Ball
(paper)

A Tale Of Two Long Tails
Daniel D'Souza, Zach Nussbaum, Chirag Agarwal, Sara Hooker
(paper)

The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
​Oreva Ahia, Julia Kreutzer, Sara Hooker
(paper)

Moving beyond “algorithmic bias is a data problem”
​Sara Hooker
(paper)

Randomness In Neural Network Training: Characterizing The Impact of Tooling
Donglin Zhuang, Xingyao Zhang, Shuaiwen Leon Song, Sara Hooker
(paper)

Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Kale-ab Tessera, Sara Hooker, Benjamin Rosman
(paper)

The Hardware Lottery
Sara Hooker
(paper)

Characterizing and Mitigating Bias in Compact Models
Sara Hooker*, Nyalleng Moorosi*, Gregory Clark, Samy Bengio, Emily Denton
(paper)

Estimating Example Difficulty using Variance of Gradients​
Chirag Agarwal, Daniel D'Souza, Sara Hooker
(paper)

What do compressed deep neural networks forget?
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, Andrea Frome
(website,paper,code)

​The State of Sparsity in Deep Neural Networks
Trevor Gale, Erich Elsen, Sara Hooker
(paper, code)

A Benchmark for Interpretability Methods in Deep Neural Networks
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim
(slides, code, paper)

The (Un)reliability of Saliency Methods 
Pieter-Jan Kindermans*, Sara Hooker*, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim
Paper, Slides.