Research Tools: code, datasets, & models

These images and examples are meant to illustrate how to build containers for use in the AI for Earth API system.

Dialogue Response Ranking Training with Large-Scale Human Feedback Data

Multi-species bioacoustic classification using deep learning algorithms. With audio recordings collected from rainforests in Puerto Rico, we build a deep learning model that combines transfer learning and pseudo-labeling as a data augmentation technique to: 1)…

This is a deep learning toolbox to train models on medical images (or more generally, 3D images). It integrates seamlessly with cloud computing in Azure.

SMCalFlow is a large English-language dialogue dataset, featuring natural conversations about tasks involving calendars, weather, places, and people. Each turn is annotated with an executable dataflow program featuring API calls, function composition, and complex constraints…

This repository contains the code and models necessary to replicate the results of our recent paper: Denoised Smoothing: A Provable Defense for Pretrained Classifiers Hadi Salman, Mingjie Sun, Greg Yang, Ashish Kapoor, J. Zico Kolter…

This AI for Earth project, in collaboration with the Wildlife Conservation Society Colombia (WCS Colombia (opens in new tab)) was developed to create up-to-date land cover maps of the Orinoquía (opens in new tab) region…

OneFuzz enables continuous developer-driven fuzzing to proactively harden software prior to release. With a single command, which can be baked into CICD, developers can launch fuzz jobs from a few virtual machines to thousands of…

The Backward Compatibility ML library is an open-source project for evaluating AI system updates in a new way for increasing system reliability and human trust in AI predictions for actions. This project’s series of loss…

This is a library for interacting with the Microsoft Embedded Social API in your Swift code.