Researcher tools: code & datasets

I give a tutorial on text to speech in ISCSLP 2021.

This project develops a formally verified implementation of the DICE measured boot protocol for IoT devices, micro-controllers, and other devices (microsoft/RIoT). The implementation is provably memory safe, functionally correct, and resistant to a class of side-channel attacks. The development and associated mathematical proofs are done in the F* language, and the verified code is extracted to efficient and portable C. DICE* has been tested on a real micro-controller device with binary size and boot timings…

Microsoft Vision Model ResNet-50 is a state-of-the-art ResNet-50 model pretrained with web-scale data, multi-task training, and web-supervision.

This repository contains the source code for the AAAI paper “Meta Label Correction for Noisy Label Learning”.

About In-memory key-value store for testing applications against weak behaviors of a database.

Sample code for the paper Estimating α-Rank by Maximizing Information Gain.

This framework exploits the sensitivity of modern machine learning algorithms to input perturbations in order to design “robust objects,” i.e., objects that are explicitly optimized to be confidently detected or classified. We demonstrate the efficacy of the framework on a wide variety of vision-based tasks ranging from standard benchmarks, to (in-simulation) robotics, to real-world experiments.

FLAML is a Python library designed to automatically produce accurate machine learning models with low computational cost. It frees users from selecting learners and hyperparameters for each learner. FLAML is powered by a new, cost-effective hyperparameter optimization and learner selection method invented by Microsoft Research.

Fast key-value store and log with support for larger-than-memory data.

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 were developed as a collaboration between MSR and the Azure Durable Functions team. It currently uses Azure Storage, Azure EventHubs, and FASTER as its storage components, but may rely on other components in the future.