Skip to main navigation
Skip to search
Skip to main content
Johns Hopkins University Home
Search content at Johns Hopkins University
Home
Profiles
Research units
Research output
Sparse-Coding Variational Autoencoders
Victor Geadah
, Gabriel Barello
, Daniel Greenidge
,
Adam S. Charles
, Jonathan W. Pillow
Whiting School of Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Sparse-Coding Variational Autoencoders'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Sparse Coding
100%
Variational Autoencoder
100%
Model Code
50%
Image Patch
25%
Recognition Model
25%
Test Performance
12%
Neural Activity
12%
Visual Pathway
12%
Response Properties
12%
Visual System
12%
Linear Projection
12%
Prior Distribution
12%
Neural Response
12%
Natural Stimuli
12%
Fitting Method
12%
Image pixels
12%
Inference Methods
12%
Novel Solutions
12%
Nonlinear Objective Function
12%
Over-complete
12%
Approximate Inference
12%
Nonlinear Properties
12%
Deep Neural Network
12%
Sparse Sets
12%
Deep Network
12%
Natural Images
12%
Recognition Network
12%
Latent Representation
12%
Recurrent Dynamics
12%
Overcomplete Dictionary
12%
Evidence Lower Bound
12%
Image Dataset
12%
Latent Dimensions
12%
Autoencoder Framework
12%
Encoder-decoder Network
12%
Computer Science
Variational Autoencoder
100%
Test Performance
20%
Objective Function
20%
Deep Neural Network
20%
Inference Method
20%
Recognition Network
20%