Skip to main navigation
Skip to search
Skip to main content
University of Haifa Home
Update your profile
Link opens in a new tab
Search content at University of Haifa
Home
Researchers
Research units
Research output
A JOHNSON-LINDENSTRAUSS FRAMEWORK FOR RANDOMLY INITIALIZED CNNS
Ido Nachum
, Jan Hazła
, Michael Gastpar
, Anatoly Khina
Research output
:
Contribution to conference
›
Paper
›
peer-review
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'A JOHNSON-LINDENSTRAUSS FRAMEWORK FOR RANDOMLY INITIALIZED CNNS'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Convolutional Neural Network
100%
Johnson-Lindenstrauss
100%
Cnns
100%
Fully Convolutional Neural Network
75%
ReLU Activation Function
75%
Johnson-Lindenstrauss Lemma
50%
Two-input
50%
Neural Network
25%
Geometric Representation
25%
Natural Images
25%
Geometric Framework
25%
Correlated Inputs
25%
Contracting Behavior
25%
Mathematics
Neural Network
100%
Lindenstrauss
100%
Convolutional Neural Network
100%
Nonlinear
25%
Geometric Representation
25%
Gaussian Distribution
25%
Natural Image
25%
Computer Science
Neural Network
100%
Convolutional Neural Network
100%
Geometric Representation
25%
Correlated Gaussian Input
25%
Biochemistry, Genetics and Molecular Biology
Gaussian Distribution
100%