Search Results for author: D. Sculley

Found 20 papers, 6 papers with code

Evaluating Prediction-Time Batch Normalization for Robustness under Covariate Shift

no code implementations19 Jun 2020 Zachary Nado, Shreyas Padhy, D. Sculley, Alexander D'Amour, Balaji Lakshminarayanan, Jasper Snoek

Using this one line code change, we achieve state-of-the-art on recent covariate shift benchmarks and an mCE of 60. 28\% on the challenging ImageNet-C dataset; to our knowledge, this is the best result for any model that does not incorporate additional data augmentation or modification of the training pipeline.

Data Augmentation

Population-Based Black-Box Optimization for Biological Sequence Design

no code implementations ICML 2020 Christof Angermueller, David Belanger, Andreea Gane, Zelda Mariet, David Dohan, Kevin Murphy, Lucy Colwell, D. Sculley

The cost and latency of wet-lab experiments requires methods that find good sequences in few experimental rounds of large batches of sequences--a setting that off-the-shelf black-box optimization methods are ill-equipped to handle.

Fair treatment allocations in social networks

no code implementations1 Nov 2019 James Atwood, Hansa Srinivasan, Yoni Halpern, D. Sculley

Simulations of infectious disease spread have long been used to understand how epidemics evolve and how to effectively treat them.


Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift

2 code implementations NeurIPS 2019 Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D. Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, Jasper Snoek

Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.

Probabilistic Deep Learning

Avoiding a Tragedy of the Commons in the Peer Review Process

no code implementations18 Dec 2018 D. Sculley, Jasper Snoek, Alex Wiltschko

In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service.

BriarPatches: Pixel-Space Interventions for Inducing Demographic Parity

no code implementations17 Dec 2018 Alexey A. Gritsenko, Alex D'Amour, James Atwood, Yoni Halpern, D. Sculley

We introduce the BriarPatch, a pixel-space intervention that obscures sensitive attributes from representations encoded in pre-trained classifiers.

Rapid Prediction of Electron-Ionization Mass Spectrometry using Neural Networks

no code implementations21 Nov 2018 Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley

When confronted with a substance of unknown identity, researchers often perform mass spectrometry on the sample and compare the observed spectrum to a library of previously-collected spectra to identify the molecule.

BIG-bench Machine Learning

AutoGraph: Imperative-style Coding with Graph-based Performance

no code implementations16 Oct 2018 Dan Moldovan, James M Decker, Fei Wang, Andrew A Johnson, Brian K. Lee, Zachary Nado, D. Sculley, Tiark Rompf, Alexander B. Wiltschko

In machine learning, imperative style libraries like Autograd and PyTorch are easy to write, but suffer from high interpretive overhead and are not easily deployable in production or mobile settings.

BIG-bench Machine Learning

No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World

no code implementations22 Nov 2017 Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, D. Sculley

Further, we analyze classifiers trained on these data sets to assess the impact of these training distributions and find strong differences in the relative performance on images from different locales.

General Classification

Direct-Manipulation Visualization of Deep Networks

no code implementations12 Aug 2017 Daniel Smilkov, Shan Carter, D. Sculley, Fernanda B. Viégas, Martin Wattenberg

The recent successes of deep learning have led to a wave of interest from non-experts.

AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech

no code implementations28 Nov 2016 Brian Patton, Yannis Agiomyrgiannakis, Michael Terry, Kevin Wilson, Rif A. Saurous, D. Sculley

Developers of text-to-speech synthesizers (TTS) often make use of human raters to assess the quality of synthesized speech.

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