An index of datasets, SDKs, APIs and other open source code created by Microsoft researchers and shared with the broader academic community. We also maintain a collection highlighting some of the tools you’ll find here.
BridgeData V2
BridgeData V2-compatible set of robotic manipulation trajectories collected at Microsoft Research. This download contains a set of object manipulation trajectories collected at Microsoft Research on a WidowX-250 robot in a setup and format compatible with…
Research Analysis Tools
Rats is a collection of tools to help researchers define and run experiments. It is designed to be a modular and extensible framework currently supporting building and running pipelines, integrating configs and services.
MofDiff
MOFDiff is a diffusion model for generating coarse-grained MOF structures. This codebase also contains the code for deconstructing/reconstructing the all-atom MOF structures to train MOFDiff and assemble CG structures generated by MOFDiff.
AI Controller Interface (AICI)
The AI Controller Interface is a system design and implementation that enables customer user code (AI Controllers, implemented as light-weight virtual machines) to tightly, efficiently, and securely integrate with LLM decoding in a cloud service.…
UDOP
UDOP adopts an encoder-decoder Transformer architecture based on T5 for document AI tasks like document image classification, document parsing and document visual question answering. You can use the model for document image classification, document parsing…
Node Engine
Node Engine is a Python service that executes a computational flow. It is designed for rapid prototyping of services and applications, e.g. used as a chatbot service in a larger system. Each call to the service…
Diffy Config Analyzer
Diffy is a research prototype tool that analyzes JSON configuration files. The goal of Diffy is to assist with configuration management. It compares JSON files and warns about potential issues by finding configuration settings that…
Neural Invariant Ranker
Official code release of our EMNLP 2023 work NeuralInvariantRanker. We have designed a ranker that can distinguish between correct inductive invariants and incorrect attempts based on the problem definition. The ranker is optimized as a…