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Questions tagged [regularization]

The tag has no usage guidance.

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Importing Excel format data into R/R Studio and using glmnet package?

I have no problem importing Excel formatted data into R/R Studio and use all other R packages that I use. But, when I want to use the glmnet package to develop a regularization model, I invariably ...
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Keras regularizers (kernel, bias and activity) vs tf.contrib.layers.apply_regularization

I have a DCGAN set up in tensorflow that is working well on the faces in the wild dataset. As an experiment, I tried using the same architecture in keras to better understand the difference in ...
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0answers
24 views

How does L1 Regularization work in lightGBM

From the paper, lightGBM does a subsampling according to sorted $|g_i|$, where $g_i$ is the gradient (for the loss function) at a data instance. My question is that, when the objective is L1 loss/...
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1answer
7 views

R package clogitL1 no longer available? [closed]

When I try to install clogitL1 on my work server I get ...
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13 views

Why do we reduce magnitude of the coefficient in regression

Why do we reduce the magnitude of the coefficient in regression? how does it help the model?
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13 views

Make embedding more Gaussian-like

I am trying to train a neural network to find a mapping(embedding) to a lower dimensional space. I would like for my dataset, once mapped to the lower dimensional space, to appear gaussian-like ...
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1answer
26 views

Loss and Regularization inference

I'm building a Matrix Factorization model for MovieLens dataset with batch-wise training. Loss function for the batch: $$ L_{batch} = 1/|B|\sum_{(u,i)\in{B}}(r_{ui} - \mu - b_u - b_i - p_u^Tq_i)^2 + \...
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46 views

Regularization in Embedding models?

What is the best way to regularize latent embeddings, I have two solution in my mind but I'm not sure which one to use over other. In batch-wise training regularize over the whole embedding matrix, ...
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37 views

Matrix Factorization for Recommender Systems

Referring to the paper Matrix Factorization Techniques for Recommender Systems, Loss function for Matrix Factorization using bias terms is given as: $$ \min_{p, q, b}\sum_{(u,i)\in\kappa}(r_{ui} - \mu ...
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1answer
40 views

How does a Bayes regularization works?

I'm trying to get grasp of Bayes regularization algorithm. List of symbols 1st: $F$ - objective function $\gamma$ - regularization parameter $M$ - number od neural network weights $N$ - number of data ...
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59 views

Regularization term in Matrix Factorization

I'm trying to build a naive recommender system using latent factor model for MovieLens dataset. From the observed set of ratings I'm trying to build a model which will decompose the sparse matrix to N ...
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32 views

Weight decay and slope of likelihood?

I came across these problems when reading Deep Learning (Ian Goodfellow et al), section "Regularization and Under-constrained problems". 1) If a weight vector w is able to achieve perfect ...
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Strategies for training DNNs with small datasets

Not every DNN is trainable for personal researchers mostly because of the huge amount of data and massive computation demand. Recently I'm working on a DNN which requires huge training data(about ...
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1answer
14 views

What affects the magnitude of lasso penalty of a feature?

Is there a way to intuitively tell if the lasso penalty for a particular feature will be small or large? Consider the following scenario: Imagine we use Lasso regression on a dataset of 100 features ...
2
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1answer
217 views

Which regularization in convolution layers (conv2D) [closed]

I am using Keras for a project. I would like to know if it makes any sense to add any kind of regularization components such as kernel, bias or activity regularization in convolutional layers i.e <...
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1answer
282 views

Regularization in simple math explained

I read a lot of articles online about how regularization works and most of them just show the equations with regularization terms but did not use example numbers to explain how the coefficient values ...
3
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1answer
120 views

Implemented early stopping but came across the error SGDClassifier: Not fitted error in sklearn

Below is the simpler implementation of early stopping which i came across the book and wanted to try it. ...
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1answer
40 views

Does Orange scale the data automatically for the linear regression with Ridge regularization

I'm using the linear regression tool with the Ridge regularization. To use the Ridge regularization I have to scale the data first. Does Orange scale the data automatically? I can't find any ...
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98 views

Is regularization only for regression?

I am making a classification model. I understand that the regularization minimizes loss functions. Can i use regularization techniques to minimize loss function in classification if doing so is ...
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1answer
2k views

When should one use L1, L2 regularization instead of dropout layer, given that both serve same purpose of reducing overfitting?

In Keras, there are 2 methods to reduce over-fitting. L1,L2 regularization or dropout layer. https://keras.io/regularizers/ https://keras.io/layers/core/#dropout What are some situations to use L1,...
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1answer
461 views

Should highly correlated features be omitted before applying Lasso?

I would greatly appreciate if you could let me know whether I should omit highly correlated features before using Lasso logistic regression (L1) to do feature ...
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4answers
2k views

Why does adding a dropout layer in Keras improve machine learning performance, given that dropout suppresses some neurons from the model?

If removing some neurons result in a better performing model, why not use a simpler neural network with fewer layers, fewer neurons in the first place? Why build a bigger, more complicated model in ...
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1answer
498 views

Is regularization included in loss history Keras returns?

I'm getting to know Keras. Right now, I'm testing with regularization and how to use them. Comparing the results of loss history for a training session with and without regularization, it seems to me ...
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0answers
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Problems with Graphical Lasso

I'm trying to use the Graphical Lasso algorithm (more specifically the R package glasso) to find an estimated graph representing the connections between a set of nodes by estimating a precision matrix....
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Why should each layer's child network output be close to parent network's output for variance regularizer?

I am reading up on PEA (Pseudo ensemble agreement) regularizer. specificaly in the neural networks domain. It introduces the concept of perturbing the model a little and forcing the model to make ...
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1answer
93 views

Why don't we want Autoencoders to perfectly represent their training data?

From Ian Goodfellow's Deep Learning Book: If an autoencoder succeeds in simply learning to set g(f(x)) = x everywhere, then it is not especially useful. Instead, ...
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1answer
46 views

What is the intuition behind Ridge Regression and Adapting Gradient Descent algorithms?

So I was going through Adaptive Gradient Descent, and learning the intuition behind it: optimizing the learning algorithm, and getting the model to converge faster. The way AdaGrad does this, is by ...
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1answer
53 views

Loss for CNN decreases and settles but training accuracy does not improve

I am training a CNN with 2 conv layers 2 Relu and max pooling and 2 FC layers the last of which has only 2 units since it's a binary classification problem. The images are spatio-temporal continuous, ...
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1answer
90 views

GANs and grayscale imagery colorization

I am currently studying colorization of grayscale satellite imagery as part of my Master's internship. After looking for various machine learning techniques, I quickly decided to go for deep learning, ...
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1answer
122 views

Ridge and Lasso Regularization

Recently, I started working on Ridge and Lasso regularization for Linear and Logistic Regression. My doubts are given below: Is the penalty the same (by same proportion) for all the coefficients or ...
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0answers
83 views

which regression model is better for one-hot encoding features (words) value

I am using the regression model to extract the importance of words in the texts with click rate value. I used the words one-hot encoding value to build features. For example, extracted all words of ...
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1answer
53 views

Should I update my regularisation L1 and L2 regularisation parameters in online setting?

I have been working on online learning for a few weeks now, especially with Vowpal Wabbit and logistic regression. My understanding of the online learning algorithms and the problem is alright but I ...
2
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1answer
71 views

Dropout in other machine learning models

Dropout is a widely used technique in deep learning. Dropout was built for neural networks, but I wonder if other prediction models can use this idea as well as a regularizer. Do you know of any ...
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1answer
971 views

Dropout vs weight decay

Dropout and weight decay are both regularization techniques. From my experience, dropout has been more widely used in the last few years. Are there scenarios where weight decay shines more than ...
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1answer
19 views

Point of dropping weights in mini batch for purpose of regularization

I have been reading "drop" is a method to regularize model better. It's purpose is to update only some % of weights in backprop and it helps you to not over fit the model. But I am wondering, is this ...
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1answer
29 views

Can the 'bin size' in a histogram be thought of as a regularity constraint?

When thinking about a histogram as an estimate of the density function, is it reasonable to think of the bin size as a parameter that constrains the local structure of that function? Also, is there a ...
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1answer
351 views

trying to decrease overfitting with regularisation in CNN

I am doing transfer learning by retraining the publicly available inception layer, without regularisation here are my initial parameters and results: ...
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0answers
176 views

Concrete Dropout for Recurrent Neural Networks (Keras)

I would like to use the Concrete Dropout Framework from GAL in application to recurrent neural networks. There is a great paper about it and the implementation can be found on the website (Thank you ...
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0answers
71 views

Custom regularisation for logistics regression

My understanding of l2 regularisation: Weights of the model are assumed to have a prior guassian distribution centered around 0. Then MAP estimate over data adds an extra penalty in cost function. ...
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1answer
469 views

Weight decay in neural network

I have been reading through this book and am trying to do the exercises. The problem is "Connecting regularization and the improved method of weight initialization" part 3. We have to use a heuristic ...
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1answer
49 views

How to think about prediction error that is not convex in hyperparameter, or over the course of training

Take the following case of a hyperparameter and prediction error: Imagine that the hyperparameter is a L2 penalty or a dropout rate -- something that we think that should have a single sweet spot -- ...
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1answer
57 views

Dropout without the averaging

The final step in dropout regularization is to multiply the weights by the dropout probability. This is motivated by analogy to bagging: averaging the weights of multiple nets. But it isn't truly that ...
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0answers
114 views

Why is there no implementation of LARS in sklearn's LogisticRegression?

Following up on Efficient L1 Regularized Logistic Regression - Su-In Lee, Honglak Lee, Pieter Abbeel and Andrew Y. Ng Why is there no option for LARS as a solver for L1 penalized LogisticRegression ...
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1answer
1k views

Which regularizer to use to get a sparse set of regression parameters?

I am doing a regression and I want to use the regularizer that will be the most useful to get a sparse set of parameters. Which regularizer should I use ? Cardinality? maximum value ? Sum of absolute ...
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1answer
107 views

Support vector machine margin term, norm or norm squared?

$$ \lambda||\hat w||^2 +(1/n)\sum max(0,1-y_i(\hat w \hat x_i -b)) $$ we know that $2/||\hat w||$ is the width of the margin. The second term penalizes a misclassified point for how far away it ...
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2answers
2k views

Why using L1 regularization over L2?

Conducting a linear regression model using a loss function, why should I use $L_1$ instead of $L_2$ regularization? Is it better at preventing overfitting? Is it deterministic (so always a unique ...
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1answer
795 views

Shouldn't L2 regularization be normalized for the number of nodes in a layer?

I'm naively thinking it would be better to normalize L2 regularization to the number of elements in a tensor, but I don't see anyone doing that. What am I missing? I'd say in a multi layer fully ...
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1answer
119 views

Should I set higher dropout prob if there are plenty of data?

I have some excessive amount of data for the size of NN I am able to teach in a reasonable time. If I feed all the data into the network it stops learning at some point and a resulting model shows ...
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0answers
892 views

Lasso implementation in Python

I am working on this course on Machine Learning 2012 from UBC (CPSC 340). I am stuck on a Homework code problem which shows the following RuntimeError in ...
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0answers
58 views

Selection of co related variables for ridge regression

I'm a newbie to machine learning. I'm working on a dataset to predict a target variable in terms of the independent variables. In the dataset the independent variables few are very highly correlated . ...