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.
Time series generation technology plays a vital role in alleviating data scarcity, especially in scenarios where collecting real-world data is expensive, time-consuming, or impractical. It also enables privacy-preserving analysis by producing realistic but non-identifiable synthetic…
The RAS library is an open-source implementation of Regional-Adaptive Sampling (RAS), a novel diffusion model sampling strategy that introduces regional variability in sampling steps. Unlike conventional methods that uniformly process all image regions, RAS dynamically…
This is the official repository of paper “Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching.” We propose Distilled Decoding (DD) to distill a pre-trained image auto-regressive model to few steps for…
This is the official code repository for the paper “Unearthing Skill-level Insights for Understanding Tradeoffs of Foundation Models”. All rationales, localized skills, and skill-slices for the 12 datasets studied in the paper can also be accessed…
This is the official codebase for the paper “The Belief State Transformer”, based on the nanoGPT repository by Andrej Karpathy.
debug-gym is a text-based interactive debugging framework, designed for debugging Python programs.
In this work, we developed a low-cost, smartphone-based automated Non-Invasive Break-Up Time (NIBUT) measurement prototype. It can be used to estimate dryness of the eye. We utilized the 3D-printed Placido ring attachment on a smartphone’s…
PromptWizard is a self-evolving framework that automates prompt optimization by iteratively refining instructions and in-context examples using feedback from LLMs. It jointly optimizes prompts and examples, incorporates expert reasoning, and adapts dynamically to diverse tasks—delivering…
MatterGen is a generative model for inorganic materials design across the periodic table that can be fine-tuned to steer the generation towards a wide range of property constraints.
