Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [dropout]

Dropout is a technique to reduce overfitting during the training phase of a neural network. DO NOT use this tag for dropout as in censoring or missing data in survival analysis or longitudinal data analysis.

Dropout is a technique to reduce overfitting during the training phase of a neural network.

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Where should I place dropout layers in a neural network?

Is there any general guidelines on where to place dropout layers in a neural network?
Franck Dernoncourt
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Are pooling layers added before or after dropout layers?

I'm creating a convolutional neural network (CNN), where I have a convolutional layer followed by a pooling layer and I want to apply dropout to reduce overfitting. I have this feeling that the dropout layer should be applied after the pooling…
pir
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What is the difference between dropout and drop connect?

What is the difference between dropout and drop connect? AFAIK, dropout randomly drops hidden nodes during training but keeps them in testing, and drop connect drops connections. But isn't dropping connections equivalent to dropping the hidden…
Machina333
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Boosting neural networks

Well recently I was working on learning boosting algorithms, such as adaboost, gradient boost, and I have known the fact that the most common used weak-learner is trees. I really want to know are there some recent successful examples (I mean some…
user4380802
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How to explain dropout regularization in simple terms?

If you have a half page to explain dropout, how would you proceed? Which is the rationale behind this technique?
Davide C
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Dropout makes performance worse

I am playing with dropout since all state of the art results in machine learning seem to be using it (for example, see here). I am familiar with all the guidelines (train longer, increase capacity of the model, use higher learning rates), but still…
Yuri
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How is Spatial Dropout in 2D implemented?

This is with refernce to the paper Efficient Object Localization Using Convolutional Networks, and from what I understand the dropout is implemented in 2D. After reading the code from Keras on how the Spatial 2D Dropout is implemented, basically a…
infomin101
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Dropout: scaling the activation versus inverting the dropout

When applying dropout in artificial neural networks, one needs to compensate for the fact that at training time a portion of the neurons were deactivated. To do so, there exist two common strategies: scaling the activation at test time inverting…
Franck Dernoncourt
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How to Identify Overfitting in Convolutional Neural network?

I understand that dropout is used to reduce over fitting in the network. This is a generalization technique. In convolutional neural network how can I identify overfitting? One situation that I can think of is when I get training accuracy too high…
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dropout: forward prop VS back prop in machine learning Neural Network

Regarding dropout, we know that in the forward propagation some neurons are put to "zero" (i.e., turned off). How about back propagation ? Are these dropped out neurons also zeros (turned off) during back-prop ? Thank Refer to this link, which…
Bill Ancalagon the black
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Dropout in Linear Regression

I've been reading the original paper on dropout, (https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf) and in the linear regression section, it is stated that: $\mathbb{E}_{R\sim Bernoulli(p)}\left[\| y\ - (R*X)w\|^2\right]$ reduces to: $\|y -…
doug
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Are early stopping and dropout sufficient to regularize the vast majority of deep neural networks in practice?

There are so many regularization techniques, it's not practical to try out all combinations: l1/l2 max norm dropout early stopping ... It seems that most people are happy with a combination of dropout + early stopping: are there cases where using…
MiniQuark
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What if all the nodes are dropped when using dropout?

When implementing dropout (or drop connect) - do you need to account for the case that every node in a layer is dropped? Even though this is a very small chance, what is the correct approach to take in this scenario? Pick a new random set to drop…
Dan
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What is the intuition for dropout used in convolutional neural networks?

Can someone give an intuition behind drop-out method used in convolutional neural networks? What is exactly drop-out doing?
Hossein
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Alternatives to L1, L2 and Dropout generalization

I have the following setup for a Finance/Machine Learning research project at my university: I'm applying a (Deep) Neural Network (MLP) with the following structure in Keras/Theano to distinguish outperforming stocks (label 1) from underperforming…
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