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.
Intelligence Toolkit
The Intelligence Toolkit is a suite of interactive workflows for creating AI intelligence reports from real-world data sources. The toolkit is designed to help users identify patterns, answers, relationships, and risks within complex datasets, with…
MetaOpt: Towards efficient heuristic design with quantifiable and confident performance
MetaOpt is the first general-purpose and scalable tool that enables users to analyze a broad class of heuristics through easy-to-use abstractions that apply to a broad range of practical heuristics. For more information, checkout MetaOpt’s project webpage and…
VisEval
VisEval: A NL2VIS Benchmark. VisEval is a benchmark designed to evaluate visualization generation methods. In this repository, we provide both the toolkit to support the benchmarking, as well as the data used for benchmarks.
DOSA
A dataset of social artifacts from different Indian geographical subcultures. This repo hosts the code to run experiments on the DOSA dataset.
MunTTS: A Text-to-Speech System for Mundari
We present MunTTS, an end-to-end text-to-speech (TTS) system specifically for Mundari, a low-resource Indian language of the Austo-Asiatic family. Our work addresses the gap in linguistic technology for underrepresented languages by collecting and processing data to…
TE-CCL
TE-CCL is a tool to generate collective communication schedules for large topologies using a Traffic Engineering-based solver. TE-CCL takes in a topology and collective (e.g. AllGather) and outputs a schedule (in JSON) detailing data transfer…
Private Benchmarking
A platform that enables users to perform private benchmarking of machine learning models. The platform facilitates the evaluation of models based on different trust levels between the model owners and the dataset owners.
MInference: Accelerating Pre-filling for Long-context LLMs via Dynamic Sparse Attention
MInference 1.0 leverages the dynamic sparse nature of LLMs’ attention, which exhibits some static patterns, to speed up the pre-filling for long-context LLMs. It first determines offline which sparse pattern each head belongs to, then…