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Learning latent superstructures in variational autoencoders for deep multidimensional clustering
Xiaopeng LI
, Zhourong CHEN
, Kin Man POON
, Nevin L. ZHANG
Department of Mathematics and Information Technology (MIT)
Research output
:
Contribution to conference
›
Poster
28
Citations (Scopus)
Overview
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Dive into the research topics of 'Learning latent superstructures in variational autoencoders for deep multidimensional clustering'. Together they form a unique fingerprint.
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Mathematics
Clustering
100%
Deep Learning
100%
Dimensional Data
100%
Gaussian Mixture Model
100%
Tree Structure
100%
Keyphrases
Copyright
25%
Deep Learning Methods
25%
Discrete Latent Variables
25%
Gaussian Mixture Model
25%
High-dimensional Data
25%
Latent Feature
50%
Latent Variables
75%
Multidimensional Clustering
100%
Multiple Partitions
25%
Tree Structure
25%
Variational Autoencoder
100%
Psychology
Clustering
100%
Gaussian Distribution
100%
Mixture Model
100%
Computer Science
Autoencoder
100%
Deep Learning
25%
Gaussian Mixture Model
25%
High Dimensional Data
25%
Multiple Partition
25%
Chemical Engineering
Deep Learning
100%