An index of datasets, SDKs, APIs and other open source code created by Microsoft researchers and shared with the broader academic community. We also maintain a collection highlighting some of the tools you’ll find here.
We introduce such a programming language, which allows logical interactions between potentially undetermined proteins and genes to be expressed in a modular manner. Programs can be translated by a compiler into sequences of biological parts,…
InterpretML is an open-source python package for training interpretable models and explaining blackbox systems. Historically, the most intelligible models were not very accurate, and the most accurate models were not intelligible. Microsoft Research has developed…
Multi-task learning toolkit for natural language understanding, including knowledge distillation.
A Python package of accelerated first-order algorithms for solving relatively-smooth convex optimization problems. It implements all algorithms described in our recent paper on accelerated Bregman proximal gradient methods, including the baseline algorithms for comparison. It…
DiCE is a Python library to explain an ML model such that the explanation is truthful to the model and yet interpretable to people. This connects to the “Explainable AI systems” theme.
A high-resolution convolutional network backbone, better than ResNets. Many computer vision applications: ImageNet classification, semantic segmentation, object detection, and facial landmark detection.
The project is an official implementation of our CVPR2019 paper “Deep High-Resolution Representation Learning for Human Pose Estimation”
