<--- Back to Details
First PageDocument Content
Computational statistics / Machine learning / Computational biology / Sepp Hochreiter / Artificial neural networks / Computational neuroscience / Deep learning / Autoencoder / Principal component analysis / Normal distribution / Stochastic gradient descent / Support vector machine
Date: 2015-12-03 06:11:45
Computational statistics
Machine learning
Computational biology
Sepp Hochreiter
Artificial neural networks
Computational neuroscience
Deep learning
Autoencoder
Principal component analysis
Normal distribution
Stochastic gradient descent
Support vector machine

Rectified Factor Networks

Add to Reading List

Source URL: www.bioinf.jku.at

Download Document from Source Website

File Size: 3,70 MB

Share Document on Facebook

Similar Documents

Syntax-Directed Variational Autoencoder for Molecule Generation Hanjun Dai1* , Yingtao Tian2* , Bo Dai1 , Steven Skiena2 , Le Song1 1 College of Computing, Georgia Institute of Technology 2

DocID: 1vlR1 - View Document

Semi-Supervised Recursive Autoencoder Si Chen and Yufei Wang Department of Electrical and Computer Engineering University of California San Diego {sic046, yuw176}@ucsd.edu

DocID: 1v727 - View Document

An Autoencoder Approach to Learning Bilingual Word Representations Sarath Chandar A P1 ∗ , Stanislas Lauly2 ∗ , Hugo Larochelle2 , Mitesh M Khapra3 , Balaraman Ravindran1 , Vikas Raykar3 , Amrita Saha3 1

DocID: 1v23p - View Document

On Nonparametric Guidance for Learning Autoencoder Representations Jasper Snoek University of Toronto

DocID: 1uwmD - View Document

arXiv:1610.00291v1 [cs.CV] 2 OctDeep Feature Consistent Variational Autoencoder Xianxu Hou University of Nottingham, Ningbo China

DocID: 1tDNC - View Document