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

Chrome logo Add to Chrome

Firefox logo Add to Firefox
Picture for Sara Hooker

Sara Hooker

The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation


Oct 06, 2021
Orevaoghene Ahia, Julia Kreutzer, Sara Hooker

Figure 1 for The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
Figure 2 for The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
Figure 3 for The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
Figure 4 for The Low-Resource Double Bind: An Empirical Study of Pruning for Low-Resource Machine Translation
* Accepted to Findings of EMNLP 2021 
  Access Paper or Ask Questions

A Tale Of Two Long Tails


Jul 27, 2021
Daniel D'souza, Zach Nussbaum, Chirag Agarwal, Sara Hooker

* Preliminary results accepted to Workshop on Uncertainty and Robustness in Deep Learning (UDL), ICML, 2021 
  Access Paper or Ask Questions

When does loss-based prioritization fail?


Jul 16, 2021
Niel Teng Hu, Xinyu Hu, Rosanne Liu, Sara Hooker, Jason Yosinski

Figure 1 for When does loss-based prioritization fail?
Figure 2 for When does loss-based prioritization fail?
Figure 3 for When does loss-based prioritization fail?
Figure 4 for When does loss-based prioritization fail?

  Access Paper or Ask Questions

Randomness In Neural Network Training: Characterizing The Impact of Tooling


Jun 22, 2021
Donglin Zhuang, Xingyao Zhang, Shuaiwen Leon Song, Sara Hooker

Figure 1 for Randomness In Neural Network Training: Characterizing The Impact of Tooling
Figure 2 for Randomness In Neural Network Training: Characterizing The Impact of Tooling
Figure 3 for Randomness In Neural Network Training: Characterizing The Impact of Tooling
Figure 4 for Randomness In Neural Network Training: Characterizing The Impact of Tooling
* 21 pages, 10 figures 
  Access Paper or Ask Questions

Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization


Feb 02, 2021
Kale-ab Tessera, Sara Hooker, Benjamin Rosman

Figure 1 for Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Figure 2 for Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Figure 3 for Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization
Figure 4 for Keep the Gradients Flowing: Using Gradient Flow to Study Sparse Network Optimization

  Access Paper or Ask Questions

Characterising Bias in Compressed Models


Oct 06, 2020
Sara Hooker, Nyalleng Moorosi, Gregory Clark, Samy Bengio, Emily Denton

Figure 1 for Characterising Bias in Compressed Models
Figure 2 for Characterising Bias in Compressed Models
Figure 3 for Characterising Bias in Compressed Models
Figure 4 for Characterising Bias in Compressed Models

  Access Paper or Ask Questions

The Hardware Lottery


Sep 21, 2020
Sara Hooker

Figure 1 for The Hardware Lottery
Figure 2 for The Hardware Lottery
Figure 3 for The Hardware Lottery
Figure 4 for The Hardware Lottery

  Access Paper or Ask Questions

Estimating Example Difficulty using Variance of Gradients


Aug 26, 2020
Chirag Agarwal, Sara Hooker

Figure 1 for Estimating Example Difficulty using Variance of Gradients
Figure 2 for Estimating Example Difficulty using Variance of Gradients
Figure 3 for Estimating Example Difficulty using Variance of Gradients
Figure 4 for Estimating Example Difficulty using Variance of Gradients
* Accepted to Workshop on Human Interpretability in Machine Learning (WHI), ICML, 2020 
  Access Paper or Ask Questions

Selective Brain Damage: Measuring the Disparate Impact of Model Pruning


Nov 13, 2019
Sara Hooker, Aaron Courville, Yann Dauphin, Andrea Frome

Figure 1 for Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Figure 2 for Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Figure 3 for Selective Brain Damage: Measuring the Disparate Impact of Model Pruning
Figure 4 for Selective Brain Damage: Measuring the Disparate Impact of Model Pruning

  Access Paper or Ask Questions

The State of Sparsity in Deep Neural Networks


Feb 25, 2019
Trevor Gale, Erich Elsen, Sara Hooker

Figure 1 for The State of Sparsity in Deep Neural Networks
Figure 2 for The State of Sparsity in Deep Neural Networks
Figure 3 for The State of Sparsity in Deep Neural Networks
Figure 4 for The State of Sparsity in Deep Neural Networks

  Access Paper or Ask Questions

Evaluating Feature Importance Estimates


Jun 28, 2018
Sara Hooker, Dumitru Erhan, Pieter-Jan Kindermans, Been Kim

Figure 1 for Evaluating Feature Importance Estimates
Figure 2 for Evaluating Feature Importance Estimates
Figure 3 for Evaluating Feature Importance Estimates
Figure 4 for Evaluating Feature Importance Estimates
* presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden 
  Access Paper or Ask Questions

The (Un)reliability of saliency methods


Nov 02, 2017
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T. Schütt, Sven Dähne, Dumitru Erhan, Been Kim

Figure 1 for The (Un)reliability of saliency methods
Figure 2 for The (Un)reliability of saliency methods
Figure 3 for The (Un)reliability of saliency methods
Figure 4 for The (Un)reliability of saliency methods

  Access Paper or Ask Questions