Microsoft Research – Emerging Technology, Computer, and Software Research

a graphic shows computer vision model using example of dog image

HEXA: Self-supervised pretraining with hard examples improves visual representations

Designer-centered reinforcement learning

An visual comparing public image classification APIs to the proposed denoised smoothing framework applied to a public image classification API. In the first sequence, an image of an elephant is input into a public image classification API, represented by an arrow leading to a gray square labeled as such. An arrow from the square points to a correct prediction of elephant, enclosed in a green square, with the words “no robustness guaranteed” under it. In the second sequence, an arrow points from an image of an elephant to six noisy copies of the image. An arrow then points from the copies to a square labeled “Custom-trained Denoiser,” which outputs six clean versions of the images. An arrow points from the clean copies to a square labeled “Public Image Classification API.” The classifier provides predictions for each copy, of which four correctly identify their respective images as elephant. Adjacent to the predictions is a pie chart labeled “Majority Rules!” with one-third of the pie in red and two-thirds in green. Arrows point to the output of the process: a final prediction of elephant, enclosed in a green square, and a strong robustness guarantee, denoted by the words “certified radius” enclosed in a green square.

Provably defending pretrained classifiers against adversarial examples

Learn about denoised smoothing

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Directions in ML: AutoML and Automating Algorithms

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POPL’s Most Influential Paper Award 2021 presented to Sumit Gulwani

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