Concept-Oriented Deep Learning: Generative Concept Representations

11/15/2018
by   Daniel T Chang, et al.
0

Generative concept representations have three major advantages over discriminative ones: they can represent uncertainty, they support integration of learning and reasoning, and they are good for unsupervised and semi-supervised learning. We discuss probabilistic and generative deep learning, which generative concept representations are based on, and the use of variational autoencoders and generative adversarial networks for learning generative concept representations, particularly for concepts whose data are sequences, structured data or graphs.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

06/05/2018

Concept-Oriented Deep Learning

Concepts are the foundation of human deep learning, understanding, and k...
05/04/2018

Unsupervised learning for concept detection in medical images: a comparative analysis

As digital medical imaging becomes more prevalent and archives increase ...
11/01/2019

Variational Autoencoders for Generative Modelling of Water Cherenkov Detectors

Matter-antimatter asymmetry is one of the major unsolved problems in phy...
04/14/2021

Is Disentanglement all you need? Comparing Concept-based Disentanglement Approaches

Concept-based explanations have emerged as a popular way of extracting h...
12/10/2020

Generative Deep Learning Techniques for Password Generation

Password guessing approaches via deep learning have recently been invest...
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...
02/18/2018

Ab initio Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism

To extract and learn representations leading to generative mechanisms fr...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.