Embeddings and Representation Learning for Structured Data

05/15/2019
by   Benjamin Paaßen, et al.
0

Performing machine learning on structured data is complicated by the fact that such data does not have vectorial form. Therefore, multiple approaches have emerged to construct vectorial representations of structured data, from kernel and distance approaches to recurrent, recursive, and convolutional neural networks. Recent years have seen heightened attention in this demanding field of research and several new approaches have emerged, such as metric learning on structured data, graph convolutional neural networks, and recurrent decoder networks for structured data. In this contribution, we provide an high-level overview of the state-of-the-art in representation learning and embeddings for structured data across a wide range of machine learning fields.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/28/2013

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity betw...
research
03/21/2018

Jet Charge and Machine Learning

Modern machine learning techniques, such as convolutional, recurrent and...
research
03/27/2020

word2vec, node2vec, graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data

Vector representations of graphs and relational structures, whether hand...
research
03/05/2021

Set Representation Learning with Generalized Sliced-Wasserstein Embeddings

An increasing number of machine learning tasks deal with learning repres...
research
12/18/2021

Weisfeiler and Leman go Machine Learning: The Story so far

In recent years, algorithms and neural architectures based on the Weisfe...
research
09/07/2021

Scale-invariant representation of machine learning

The success of machine learning stems from its structured data represent...
research
02/18/2020

An Overview of Distance and Similarity Functions for Structured Data

The notions of distance and similarity play a key role in many machine l...

Please sign up or login with your details

Forgot password? Click here to reset