IJCAI 2020poster0 citations
A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward)
Abstract
Learning higher-level representations from data has been on the agenda of AI research for several decades. In the paper, I will give a survey of various approaches to learning symbolic higher-level representations: feature construction and constructive induction, predicate invention, propositionalization, pattern mining, and mining time series patterns. Finally, I will give an outlook on how approaches to learning higher-level representations, symbolic and neural, can benefit from each other to solve current issues in machine learning.
Machine Learning: generalKnowledge Representation and Reasoning: general
BibTeX
@inproceedings{ijcai2020p678,
title = {A Brief History of Learning Symbolic Higher-Level Representations from Data (And a Curious Look Forward)},
author = {Kramer, Stefan},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {4868--4876},
year = {2020},
month = {7},
note = {Survey track},
doi = {10.24963/ijcai.2020/678},
url = {https://doi.org/10.24963/ijcai.2020/678},
}