Discover an index of datasets, SDKs, APIs and open-source tools developed by Microsoft researchers and shared with the global academic community below. These experimental technologies—available through Azure AI Foundry Labs (opens in new tab)—offer a glimpse into the future of AI innovation.
A toolkit for reasoning about source code (tasks related to program understanding, synthesis, and verification) using graph neural networks. Developed in partnership with MSR Cambridge. Used by several ongoing projects both inside and outside MSR.
A synthetic dataset of visual programs for the program synthesis task, now a common benchmark in the academic community. This webpage hosts the dataset for synthetically generated Karel programs that are used for training and…
A framework/SDK for program synthesis from input-output examples, with pre-built applications for data wrangling, Jupyter integration for data scientists, repetitive code editing, text manipulations, and extraction from webpages. Started at MSR, now developed by a…
Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning. There is a specific focus on reinforcement learning…
Eyes First games are popular games reinvented for an “Eyes First” experience. The games are powered by Windows 10 eye tracking APIs and can be used with or without Windows 10 Eye Control, a key…
Vale (Verified Assembly Language for Everest) is a tool for constructing formally verified high-performance assembly language code, with an emphasis on cryptographic code. It uses existing verification frameworks, such as Dafny and F*, for formal…
EverCrypt is a high-performance, cross-platform, formally verified modern cryptographic provider distributed as a combined C/ASM library. EverCrypt packages cryptographic implementations from the HACL* and ValeCrypt projects, and automatically picks the fastest one available, depending on…
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This is the data and source code download page for the paper: “Document-Level N-ary Relation Extraction with Multiscale Representation Learning” by Robin Jia, Cliff Wong and Hoifung Poon NAACL 2019.
Supporting code for the Model-Based Machine Learning book. This project contains the sample code and test data for the freely available online book on model-based machine learning published at http://mbmlbook.com/. The book aims at educating…
