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 project contains the source code of the Dynamic Neural Semantic Parser (DynSP), based on DyNet, described in the paper paper “Search-based Neural Structured Learning for Sequential Question Answering”.
EzPC is a cryptographic-cost aware compiler that generates efficient and scalable Secure Multi-party Computation (MPC) protocols using combinations of arithmetic and boolean circuits, from high-level code.
Bootstrapping is a crucial operation in Gentry’s breakthrough work on fully homomorphic encryption (FHE), where a homomorphic encryption scheme evaluates its own decryption algorithm. There has been a couple of implementations of bootstrapping, among which…
By Matthew Brehmer (Microsoft Research), Bongshin Lee (Microsoft Research), Petra Isenberg (Inria), and Eun Kyoung Choe (University of Maryland) This repository is maintained by Matthew Brehmer. It contains supplemental material for a journal paper including source code for software used in…
Marlo is a multi agent reinforcement learning platform using Project Malmo. Malmo is a platform for Artificial Intelligence experimentation and research built on top of Minecraft.
SealPIR allows a client to download an element from a database stored by a server without revealing which element was downloaded. SealPIR was introduced in our paper. SealPIR is a (research) library and should not be…
Mobile web application shell for educational content and interactive skill practices using a chat-like interface.
Project Frigatebird is aimed at enabling small fixed-wing UAVs to travel long distances by soaring — taking advantage of rising air regions the way human sailplane pilots and bird species like frigatebirds do — using…
A deep learning project in cooperation with the NOAA Marine Mammal Lab to detect & classify arctic seals in aerial imagery to understand how they’re adapting to a changing world.
This data contains our custom Mad Libs that accompany the EMNLP 2017 paper called “Filling the Blanks for Mad Libs Humor”. There are 50 Mad Libs in total. For each, we provide our original Mad…
