Research Tools: code, datasets, & models

F*: Verification system for effectful programs

This is the data and source code download page for the paper: “Document-Level N-ary Relation Extraction with Multiscale Representation Learning” by Robin Jia, Cliff Wong and Hoifung Poon NAACL 2019.

Supporting code for the Model-Based Machine Learning book. This project contains the sample code and test data for the freely available online book on model-based machine learning published at http://mbmlbook.com/ (opens in new tab). The…

This data set contains accelerometer and gyroscope recordings from over 200 participants performing various gym exercises. This data set is described in more detail in the associated manuscript: Morris, D., Saponas, T. S., Guillory, A.,…

A portable volatile memory acquisition tool for Linux. AVML is an X86_64 userland volatile memory acquisition tool written in Rust, intended to be deployed as a static binary. AVML can be used to acquire memory…

AMDIM (Augmented Multiscale Deep InfoMax) is an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context.

MazeExplorer is a customisable 3D benchmark for assessing generalisation in Reinforcement Learning. It is based on the 3D first-person game Doom and the open-source environment VizDoom. This repository contains the code for the MazeExplorer Gym…

This repository hosts the code for the following ICML 2019 paper: Dead-ends and Secure Exploration in Reinforcement Learning

We propose a framework for participants to collaboratively build a dataset and use smart contracts to host a continuously updated model.

SeeDot is an automatic quantization tool that generates efficient machine learning (ML) inference code for IoT devices. ML models are usually expressed in floating-point, and IoT devices typically lack hardware support for floating-point arithmetic. Hence,…