Researcher tools: code & datasets

This repository contains necessary data associated with the Analyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance, published in EMNLP 2021 Multilingual Representation Learning workshop. This data is a multi-lingual extension of ID 3860 released dataset. Similar to the ID 3397 and 3860, the broad goal for this project is to research and develop Neuro-Symbolic systems for Natural Language Inferencing (NLI) to leverage the “correctness” guarantees and interpretability of symbolic systems in neural network inference. In…

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.

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

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 Kit (DDK) is for 3rd party hardware designers, firmware developers and manufacturers who wish to create their own Jacdac devices. We’ll be releasing the DDK open-source.

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 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 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 decisions given incomplete information. How can we replicate those abilities in machine intelligence? In project Azua, we build AI algorithms to aid efficient decision-making with minimum data requirements. To achieve optimal trade-offs and enable the human in the loop, we…

The subseasonal_toolkit package provides implementations of the subseasonal forecasting toolkit models, machine learning models, and meteorological baselines presented in the preprint Learned Benchmarks for Subseasonal Forecasting Soukayna Mouatadid, Paulo Orenstein, Genevieve Flaspohler, Miruna Oprescu, Judah Cohen, Franklyn Wang, Sean Knight, Maria Geogdzhayeva, Sam Levang, Ernest Fraenkel, and Lester Mackey. Sep. 2021.

This is a webapp that provides the ability to visualize and explore large graphs (networks). It is based on research our team has done for graph machine learning and high-scale visualization. We are open-sourcing the code, and also plan to use the github.io site to host a running copy of the application. The application is a static website and does not upload or store any user information or cookies.