177 citations · 290 across the 4 of their papers we have counts for
4 papers
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
Shreya Shankar, Yoni Halpern, Eric Breck +3
Modern machine learning systems such as image classifiers rely heavily on large scale data sets for training. Such data sets are costly to create, thus in practice a small number o…
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…
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Heng-Tze Cheng, Zakaria Haque, Lichan Hong +12
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge machine learning for practitioners in o…
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…