430 citations · 869 across the 10 of their papers we have counts for
6 papers · 1 filter
Underspecification Presents Challenges for Credibility in Modern Machine Learning
Alexander D'Amour, Katherine Heller, Dan Moldovan +37
ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures. An ML pipeline i…
Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift
Zachary Nado, Shreyas Padhy, D. Sculley +3
Covariate shift has been shown to sharply degrade both predictive accuracy and the calibration of uncertainty estimates for deep learning models. This is worrying, because covariat…
TensorFlow.js: Machine Learning for the Web and Beyond
Daniel Smilkov, Nikhil Thorat, Yannick Assogba +17
TensorFlow.js is a library for building and executing machine learning algorithms in JavaScript. TensorFlow.js models run in a web browser and in the Node.js environment. The libra…
BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity
Alexey A. Gritsenko, Alex D'Amour, James Atwood +2
We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers. The patches encourage internal m…
Direct-Manipulation Visualization of Deep Networks
Daniel Smilkov, Shan Carter, D. Sculley +2
The recent successes of deep learning have led to a wave of interest from non-experts. Gaining an understanding of this technology, however, is difficult. While the theory is impor…
Large-Scale Learning with Less RAM via Randomization
Daniel Golovin, D. Sculley, H. Brendan McMahan +1
We reduce the memory footprint of popular large-scale online learning methods by projecting our weight vector onto a coarse discrete set using randomized rounding. Compared to stan…