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.
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
We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model.
SeeDot is an automatic quantization tool that generates efficient machine learning (ML) inference code for IoT devices. ML models are usually expressed in floating-point, and IoT devices typically lack hardware support for floating-point arithmetic. Hence,…
This data set contains 1.2M sequences of camera trap images, totaling 3.2M images. Species-level labels are provided for 48 species. We have also added approximately 100,000 bounding box annotations to approximately 38,000 images. The images…
This data set contains 3.7M camera trap images from five locations across the United States, with species-level labels for 28 species. More information about this data set is available in the associated manuscript: Tabak, M.…
Microsoft Speech Corpus (Indian languages) release contains conversational and phrasal speech training and test data for Telugu, Tamil and Gujarati languages. The data package includes audio and corresponding transcripts. Data provided in this dataset shall…
This data set contains 244,497 images from 140 camera locations in the Southwestern United States, with species-level labels for 22 species, and approximately 66,000 bounding box annotations.
TensorWatch is a comprehensive library of tools to debug and monitor training phase for Deep Learning and Reinforcement Learning models as well as perform analysis on trained models. TensorWatch is a debugging and visualization tool…
