Unsupervised Cross-Domain Prerequisite Chain Learning using Variational Graph Autoencoders

05/07/2021
by   Irene Li, et al.
8

Learning prerequisite chains is an essential task for efficiently acquiring knowledge in both known and unknown domains. For example, one may be an expert in the natural language processing (NLP) domain but want to determine the best order to learn new concepts in an unfamiliar Computer Vision domain (CV). Both domains share some common concepts, such as machine learning basics and deep learning models. In this paper, we propose unsupervised cross-domain concept prerequisite chain learning using an optimized variational graph autoencoder. Our model learns to transfer concept prerequisite relations from an information-rich domain (source domain) to an information-poor domain (target domain), substantially surpassing other baseline models. Also, we expand an existing dataset by introducing two new domains: CV and Bioinformatics (BIO). The annotated data and resources, as well as the code, will be made publicly available.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/17/2021

Efficient Variational Graph Autoencoders for Unsupervised Cross-domain Prerequisite Chains

Prerequisite chain learning helps people acquire new knowledge efficient...
research
04/22/2020

R-VGAE: Relational-variational Graph Autoencoder for Unsupervised Prerequisite Chain Learning

The task of concept prerequisite chain learning is to automatically dete...
research
10/11/2019

Relation learning in a neurocomputational architecture supports cross-domain transfer

People readily generalise prior knowledge to novel situations and stimul...
research
10/13/2022

Cross-domain Variational Capsules for Information Extraction

In this paper, we present a characteristic extraction algorithm and the ...
research
05/31/2022

Variational Transfer Learning using Cross-Domain Latent Modulation

To successfully apply trained neural network models to new domains, powe...
research
02/22/2022

Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning

In data-rich domains such as vision, language, and speech, deep learning...
research
06/15/2018

One-Shot Unsupervised Cross Domain Translation

Given a single image x from domain A and a set of images from domain B, ...

Please sign up or login with your details

Forgot password? Click here to reset