PDF-Understanding the difculty of training deep feedforward neural networks Xavier Glorot PDF document

All these experimen tal results were obtained with new initialization or training mechanisms Our objective here is to understand better why standard gradient descent

Upload by yoshiko-marsland
Published on 2014-10-22

Presentation Embed Code

<iframe width="560" height="315" src="https://www.docslides.com/embed/7037" frameborder="0" allowfullscreen></iframe>

Download Presentation

Download Presentation The PPT/PDF document " Understanding the difculty of training..." is the property of its rightful owner. Permission is granted to download and print the materials on this web site for personal, non-commercial use only, and to display it on your personal computer provided you do not modify the materials and that you retain all copyright notices contained in the materials. By downloading content from our website, you accept the terms of this agreement.

Understanding the difculty of training deep feedforward neural networks Xavier Glorot: Transcript

PDF-Understanding the difculty of training deep feedforward neural networks Xavier Glorot PDF document

Presentation Embed Code

Download Presentation

Understanding the difculty of training deep feedforward neural networks Xavier Glorot: Transcript

Related Contents

The Difculty of Training Deep Architectures and the Effect of Unsupervised PreTraining

Generalized Denoising AutoEncoders as Generative Models Yoshua Bengio Li Yao Guillaume

Understanding the Difficulty of Training Deep Feedforward Neural Networks

Y.Bengio,G.Mesnil,Y.Dauphin,S.Rifai.BetterMixingviaDeepRepresentations

Training Neural Networks

On Unsupervised Feature Learning with

Journal of Machine Learning Research Submi tted Revised Published Random Search

Multi-Layer Feedforward Neural Networks

An Introduction to Human-Aided

Lecture 2: Learning with neural networks

28th October 2011 (Canada), 4th May 2012 (UK)

Deep Learning – An Introduction

The th Workshop on the Innovative Use of NLP for Building Educational Applications pages

Learning Stochastic Feedforward Neural Networks Yichuan Tang Department of Computer Science