https://gradientscience.org/robust_apps/
Robustness Beyond Security: Computer Vision Applications – gradient science
An off-the-shelf robust classifier can be used to perform a range of computer vision tasks beyond classification.
computer vision applicationsgradient sciencerobustnessbeyondsecurity
https://gradientscience.org/modelcomponents-editing/
Editing Predictions by Modeling Model Computation – gradient science
We use our component modeling framework to design targeted model edits.
gradient scienceeditingpredictionsmodelingcomputation
https://gradientscience.org/
gradient science
Research highlights and perspectives on machine learning and optimization from MadryLab.
gradient science
https://gradientscience.org/modelcomponents/
Decomposing Predictions by Modeling Model Computation – gradient science
We introduce a framework called component modeling for studying how model components collectively shape ML predictions.
gradient sciencepredictionsmodelingcomputation
https://gradientscience.org/adv/
Adversarial Examples Are Not Bugs, They Are Features – gradient science
A new perspective on adversarial perturbations
adversarial examplesare notgradient sciencebugsfeatures
https://gradientscience.org/photoguard/
Raising the Cost of Malicious AI-Powered Image Editing – gradient science
Inspired by an episode of the Daily Show, we hacked together a technique for
the costai poweredimage editinggradient scienceraising
https://gradientscience.org/batchnorm/
How does Batch Normalization Help Optimization? – gradient science
An closer look at one of the most popular techniques in modern deep learning.
how doesbatch normalizationgradient sciencehelpoptimization
https://gradientscience.org/unadversarial/
Unadversarial Examples: Designing Objects for Robust Vision – gradient science
We show how to design objects to help, rather than hurt, the performance of vision systems; the resulting objects improve performance on natural and...
gradient scienceexamplesdesigningobjectsrobust
https://gradientscience.org/datamodels-2/
Uncovering Brittleness with Datamodels – gradient science
In the second part of our datamodels series, we use datamodels to identify and study a new form of model brittleness.
gradient scienceuncoveringbrittleness