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.
Netherite Execution Engine for Durable Functions
Netherite is a storage execution engine for the Durable Task and Durable Functions frameworks. It is designed to achieve higher throughput and lower latency than previous implementations. It contains several architectural innovations and optimizations that…
MPNet implementation in Huggingface
MPNet is supported by Huggingface, one of the most popular repo for pre-trained language models. You can follow this link to have a try on MPNet via Huggingface.
RespireNet: A Deep Neural Network for Accurate Abnormality Detection in Lung Sounds using Device specific fine-tuning
Auscultation of respiratory sounds is the primary tool for screening and diagnosing lung diseases. Automated analysis, coupled with digital stethoscopes, can play a crucial role in enabling telescreening of fatal lung diseases. Deep neural networks…
Sepsis Cohort from MIMIC III
This repo provides code for generating the sepsis cohort from MIMIC III dataset. Our main goal is to facilitate reproducibility of results in the literature.
Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift (code)
Unsupervised Domain Adaptation algorithms robust to mismatched label distributions
iPhys Toolbox
Accompanying paper: iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox A MATLAB toolbox for iPPG analysis. The toolbox includes implementations of commonly used methods.
MTTS-CAN
Accompanies the paper Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement (NeurIPS 2020)
AdversarialGMM
Code for replication of experiments in the paper: Minimax Estimation of Conditional Moment Models Nishanth Dikkala, Greg Lewis, Lester Mackey, and Vasilis Syrgkanis arXiv preprint arXiv:2006.07201 (2020)