Research Tools: code, datasets, & models

Modulo allows optimal selection of vehicles for effective drive-by sensing.

A number of PyTorch modules (neural network components) that are broadly (re)usable. The goal of this release is to provide a library to machine learning researchers to use and advance the state-of-the-art in the area.

This PyTorch package implements Taming Sparsely Activated Transformer with Stochastic Experts.

This PyTorch package implements Very Deep Transformers for Neural Machine Translation, to stabilize the large scale language model and neural machine translation training, as described in: Very deep transformers for neural machine translation

This repository contains necessary data associated with the Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance (opens in new tab), published in EMNLP 2021 Multilingual Representation Learning workshop (opens in new tab). This data is…

This is the implementation of the paper Meta Self-training for Few-shot Neural Sequence Labeling. MetaST is short for meta-learning for self-training.

Jacdac (Joint Asynchronous Communications; Device Agnostic Control) is a single wire broadcast protocol for the plug and play of microcontrollers (MCUs) within the contexts of rapid prototyping, making, and physical computing. The Jacdac Device Development…

Research code for ICCV 2021 paper “Mesh Graphormer”

This Python package implements algorithms for online learning under delay using optimistic hints. More details on the algorithms and their regret properties can be found in the manuscript Online Learning with Optimism and Delay.

Many modern AI algorithms are known to be data-hungry, whereas human decision-making is much more efficient. The human can reason under uncertainty, actively acquire valuable information from the world to reduce uncertainty, and make personalized…