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.
This is a detailed specification for the Checked C extension that explains the design in-depth.
The Checked C extension to C is being implemented in a fork of the clang compiler. You can download the latest version of the Checked C compiler for Windows from this GitHub link.…
This data set consists of timelines of geo-located Twitter activity per U.S. county over ~4 years, from Jan 1, 2011 through April 30, 2014. Each county-timeline represents the amount of Twitter activity seen coming from…
Common Runtime for Applications (CRA) is a software layer (library) that makes it easy to create and deploy distributed dataflow-style applications on top of resource managers such as Kubernetes, YARN, and stand-alone cluster execution.
SPTAG (Space Partition Tree And Graph) is a library for large scale vector approximate nearest neighbor search scenario. It assumes that the samples are represented as vectors and that the vectors can be compared by…
A benchmark dataset for training and evaluating subseasonal forecasting systems—systems predicting temperature or precipitation 2-6 weeks in advance—in the western contiguous United States.
Charticulator is an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Most existing chart construction interfaces require authors to choose from predefined chart layouts, thereby precluding the construction of novel…
This is PointSQL, the source codes of Natural Language to Structured Query Generation via Meta-Learning (opens in new tab) and Pointing Out SQL Queries From Text from Microsoft Research. We present the setup for the WikiSQL experiments.
This project contains the source code of the Dynamic Neural Semantic Parser (DynSP), based on DyNet, (opens in new tab) described in the paper paper “Search-based Neural Structured Learning for Sequential Question Answering”.
