Improved Dropout for Shallow and Deep Learning

02/06/2016
by   Zhe Li, et al.
0

Dropout has been witnessed with great success in training deep neural networks by independently zeroing out the outputs of neurons at random. It has also received a surge of interest for shallow learning, e.g., logistic regression. However, the independent sampling for dropout could be suboptimal for the sake of convergence. In this paper, we propose to use multinomial sampling for dropout, i.e., sampling features or neurons according to a multinomial distribution with different probabilities for different features/neurons. To exhibit the optimal dropout probabilities, we analyze the shallow learning with multinomial dropout and establish the risk bound for stochastic optimization. By minimizing a sampling dependent factor in the risk bound, we obtain a distribution-dependent dropout with sampling probabilities dependent on the second order statistics of the data distribution. To tackle the issue of evolving distribution of neurons in deep learning, we propose an efficient adaptive dropout (named evolutional dropout) that computes the sampling probabilities on-the-fly from a mini-batch of examples. Empirical studies on several benchmark datasets demonstrate that the proposed dropouts achieve not only much faster convergence and but also a smaller testing error than the standard dropout. For example, on the CIFAR-100 data, the evolutional dropout achieves relative improvements over 10% on the prediction performance and over 50% on the convergence speed compared to the standard dropout.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/23/2020

On Convergence and Generalization of Dropout Training

We study dropout in two-layer neural networks with rectified linear unit...
research
10/24/2020

Adam with Bandit Sampling for Deep Learning

Adam is a widely used optimization method for training deep learning mod...
research
06/12/2013

Understanding Dropout: Training Multi-Layer Perceptrons with Auxiliary Independent Stochastic Neurons

In this paper, a simple, general method of adding auxiliary stochastic n...
research
05/15/2019

Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks

In this work, we propose a novel technique to boost training efficiency ...
research
09/04/2023

Dropout Attacks

Dropout is a common operator in deep learning, aiming to prevent overfit...
research
01/21/2020

Variational Dropout Sparsification for Particle Identification speed-up

Accurate particle identification (PID) is one of the most important aspe...
research
08/10/2018

Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning

Multi-layer neural networks have lead to remarkable performance on many ...

Please sign up or login with your details

Forgot password? Click here to reset