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.
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/ (opens in new tab). The…
Use your eyes to slide all the tiles to collide like numbers to double them until you reach 2048 (or higher).
Exercise your mind and eyes with match two! Flip cards to reveal images, and match up all the pairs to win the game.
Practice your skills by using your eyes to slide the tiles back into order to solve the puzzles.
This data set contains accelerometer and gyroscope recordings from over 200 participants performing various gym exercises. This data set is described in more detail in the associated manuscript: Morris, D., Saponas, T. S., Guillory, A.,…
Learn how to dwell with your eyes and navigate Lunita through each maze to help her find her way home.
A portable volatile memory acquisition tool for Linux. AVML is an X86_64 userland volatile memory acquisition tool written in Rust, intended to be deployed as a static binary. AVML can be used to acquire memory…
AMDIM (Augmented Multiscale Deep InfoMax) is an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.
MazeExplorer is a customisable 3D benchmark for assessing generalisation in Reinforcement Learning. It is based on the 3D first-person game Doom and the open-source environment VizDoom. This repository contains the code for the MazeExplorer Gym…
This repository hosts the code for the following ICML 2019 paper: Dead-ends and Secure Exploration in Reinforcement Learning
