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.
Microsoft sponsored and co-organized Indoor Location Competition 2.0 in 2021. 1446 contestants from more than 60 countries making up 1170 teams participated in this unique global event. In this competition, a first-of-its-kind large-scale indoor location…
An open source indexing subsystem that brings index-based query acceleration to Apache Spark™ and big data workloads.
This is a codebase for our recently released paper “Focal Self-attention for Local-Global Interactions in Vision Transformers”. It developed a new sparse self-attention mechanism called focal self-attention towards more effective and efficient vision transformers. The…
The subseasonal_data package provides an API for loading and manipulating the SubseasonalClimateUSA dataset developed for training and benchmarking subseasonal forecasting models. Here, subseasonal refers to climate and weather forecasts made 2-6 weeks in advance. See…
Data augmentation is proven to be effective in many NLU tasks, especially for those suffering from data scarcity. In this paper, we present a powerful and easy to deploy text augmentation framework, Data Boost, which…
Microsoft KaggleDBQA is a cross-domain and complex evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. It also provides database documentation, which contain rich in-domain knowledge. The nature of…
Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data. Finn is a standardized modeling framework…
DS++ benchmark is designed for evaluating both classical and workload-driven database systems on modern decision support workloads. DS++ is adapted from the widely-used industrial standard TPC-DS benchmark. It enhances the TPC-DS benchmark with complex data…
This is a research project in exploring self-supervised learning (SSL) for computer vision. It aims to learn general-purpose image features from raw pixels without relying on manual supervisions, and the learned networks serve as the…
