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Questions tagged [optimization]

In statistics this refers to selecting an estimator of a parameter by maximizing or minimizing some function of the data. One very common example is choosing an estimator which maximizes the joint density (or mass function) of the observed data referred to as Maximum Likelihood Estimation (MLE).

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Does policy optimization learn policies to make better actions with higher probability? [closed]

When I talk about policy optimization, it is referred to the following picture, and it is linked to DFO/Evolution plus Policy Gradients. I would like to know is it correct to say: Policy ...
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Per parameter learning rate for AdamOptimizer by scaling gradients

I'm using an AdamOptimizer, and I compute the gradients, but before applying the descent step, I scale (i.e.: multiply) gradients with constants, to mimic having a different learning rate per ...
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Minimization algorithm that can consider gradient close to solution

I want to minimize a function which has sharp gradients close to each local minimum. Due to process tolerances, I want to find solutions which meet some minimum criterion (e.g. lower than x), but have ...
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Should the minimum value of a cost (loss) function be equal to zero?

We know optimization techniques search in the space of all the possible parameters for a parameter set that minimizes the cost function of the model. The most well-known loss functions, like MSE or ...
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Are there any learner-specific optimizers?

In reading about machine learning (ML), and working through some basic examples, it appears to me most learning algorithms use generic optimizers. I am using the word "optimizer" to describe the ...
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How to derive the sum-of squares error function formula?

I'm attending a Machine Learning course and I'm studying linear models for classification right now. Slides present approaches to learn linear discriminants (Least squares, Fisher's linear ...
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How to Define a Cost Fucntion?

I want to define a cost function in python to identify optimum value in days when i should end a marketing campaign to save spend on campaigns not generating traffic good traffic. Problem is I dont ...
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How to optimize the separation of two distributions from binary classfication

Given a sample where for each individual a classification is predetermined (e.g. sick or not) and 5 random variables are measured. The random variables are on the same scale but from differnt bins. E....
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Primal vs Dual SVM Problem for linearly separable data

Given a linearly separable data where i don't need to use kernels. Is there any need to use the dual form? In other words, is primal form enough?
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using regression model to optimize teams working on work items

I have a few work items with these features: WI1, WI2, WI3 which describe these work items. I also know the number of people and how many minutes they spend each ...
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AI that maximizes the storage of rectangular parallelepipeds in a bigger parallelepiped

As you can see in the title, I'm trying to program an AI in Java that would help someone optimize his storage. The user has to enter the size of his storage space (a box, a room, a warehouse etc...) ...
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how to calculate number of frames in epsilon greedy with decay rate?

If starting epsilon is alpha and end epsilon is beta in epsilon greedy algorithm. discount rate is gamma and epsilon decay is lambda, how to calculate the F: number of frames to reach from alpha to ...
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Questions as I'm implementing computer vision (IID), image processing paper

As I'm reading paper An L1 image transform for edge-preserving smoothing and scene-level intrinsic decomposition r/https://i.cs.hku.hk/~yzyu/publication/L1PIF-sig2015.pdf I got some unclear parts. For ...
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What are the reasons of select a optimizer to be SGD or Adam in DQN?Why?

I saw several comparison between SGD, RMSPROP and ADAM but what I am looking for is their comparsion in DQN algorithm? What is best to select as optimizer SGD or Adam in DQN? Why? Please check the ...
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Which are the latest Optimization techniques in artificial intelligence?

My project work is optimization in power system using artificial intelligence (like fault location and classification,load forecasting and context awareness and IoT etc ) and I have used PSO (particle ...
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clipping the reward for adam optimizer in keras

I would like to clip the reward in keras. I saw it is possible to clip the norm and clip the value is sgd as follows: ...
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Algorithm for campaign optimization (Digital Advertising)

Suppose i am running an Ad thru an Ad exchange A, and i have a set of campaigns running on it. I have The spend of the campaign. The budget allocated to it. The number of hours it took to exhaust it'...
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has number of output layer of DNN any effect in speed of find the optimal answer of DNN?

has number of output layer of DNN any effect in speed of find the optimal answer of DNN? For instance the more episodes is needed to train a DNN when the number of outputs is more? Is it correct?
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Policy gradient: why does this converge with Adam and not SGD?

I am looking into policy gradient methods. I stumbled into this implementation: https://gist.github.com/calclavia/cfcd41ad4e47d7b9b6ab8af15410747a It uses a Nesterov Adam optimizer. If I run it, it ...
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Why aren't Genetic Algorithms used for optimizing neural networks?

From my understanding, Genetic Algorithms are powerful tools for multi-objective optimization. Furthermore, training Neural Networks (especially deep ones) is hard and has many issues (non-convex ...
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What is new population in genetic algorithm?

Here is my (mis?)understanding of genetic algorithm: Create n individuals. This is initial population Calculate fitness of each individual in this population ...
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Why imbalanced data-set will bias the prediction model towards the more common class?

As we know, an imbalanced data-set has a disadvantage of training a model for deep learning. However, I don't know how to explain it with mathematics?
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Small dataset in Time series

I have soccer data with a time series index. 30 seconds interval. So, 194 rows for 90+ minutes per game. I have 1500 games. The dataframe has the following information. Home/Away: • Goal Total. • ...
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What is the class of this optimization problem?

I have the following optimization problem: Find $\mathbf{w}$ such that the following error measure is minimised: $E_u = \dfrac{1}{N_u}\sum_{i=0}^{N_u-1}\lVert \mathbf{w}^Tx(t_{i+1})-\mathbf{F}(\{\...
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RMSProp Optimizer Performing Poorly

I am building an RNN and have decided to try RMSProp as an alternative to sgd. Here is my implementation: ...
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Mean-variance mapping optimization (MVMO) in R

Someone know tell me if there is any package in R about Mean-variance mapping optimization (MVMO) algorithm? I already researched, but don't find anything about this.
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Train the model to increase accuracy rather than to minimise loss

I am currently in a situation of seq2seq training where the cross entropy loss is very low (near zero) but the accuracy is also very low. This made me wondering if there were any loss functions that ...
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Does stochaistic gradient descent perform better and Adam optimizer on small data sets?

I have noticed while comparing different optimization algorithms that SGD performs better than Adam optimizer when data set is smaller than a particular limit. What might be the mathematical reason ...
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Vanishing Gradient in Last Layers

While training my neural network with Adam, I'm experiencing strange behavior where vanishing gradients happen in the last layers. In my case neural network uses multidimensional input and returns ...
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Optimising Expensive Functions

I'm trying some different techniques to optimise a Boosted Gradient Regressor by using an evolutionary programming technique to try and find the most efficient set of features. So far I've been having ...
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What is the logic of the epoch?

What is the logic of the epoch? for example, 1 time, 2 times ... I just do not know what else is working to give better results than I know.
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Where can we find the application of bayes's theorem in Bayesian optimiation with gaussian processing

I am trying to learn bayesian optimisation by following this tutorial. However, until now I don't get the relation between bayes's theorem to the gaussian process formalism. Any ideas?
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Octave fminunc error: “Trust region radius became excessively small”

I am trying to run a linear regression using fminunc to optimize my parameters. However, while the code never fails, the fminunc function seems to only be running once and not converging. The exit ...
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Implementation of Fisher's extact test in Scikit-learn

How to implement efficiently Fisher's extact test in Scikit-learn to use it with SelectKBest in an optimal way ?
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How to optimize stacking?

I'm wondering if there is a way to find the optimal weights when stacking multiple models? For example if I have five models which perform similarly, how do I find if I should discard some altogether, ...
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Minimizing an upper bound of objective function

In many machine learning problems, you often have an objective function (e.g., cost, loss, error) that you want to minimize. Instead of directly minimizing this objective function, I sometimes see ...
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Optimizing an averaged perceptron algorithm using numpy and scipy instead of dictionaries

So I'm trying to write an averaged perceptron algorithm (page 48 here for the equation) in python. Instead of storing the historical weights, I simply accumulate the weights and then multiply ...
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Loss function doesn't decrease after certain number of epochs

I am using a neural network to classify pairs of inputs as being close to each other (having distance 0) or far from each other (having distance 1). I am using the following model: ...
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What does it mean for a method to be invariant to diagonal rescaling of the gradients?

In the paper https://arxiv.org/pdf/1412.6980.pdf which describes Adam: a method for stochastic optimization, the author states: The method is straightforward to implement, is computationally ...
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How does binary cross entropy work?

Let's say I'm trying to classify some data with logistic regression. Before passing the summed data to the logistic function (normalized in range $[0,1]$), weights must be optimized for desirable ...
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194 views

Linear Regression Optimization

I am learning linear regression right now. In the most of the examples of implementation of this method, which I found, gradient descent is used. Is there a better way to optimize linear regression ...
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Significance of comparing Receiver Operating Characteristic (ROC) curves

An ROC curve plots the true positive rate (sensitivity) as function of the false positive rate (100-specificity) for different cut-off points of a parameter. Each point on the ROC curve represents a ...
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When should the bias b be updated with weights w and when should it be updated seperately?

It seems in some Machine Learning models, the bias term $b$ is updated just like other weights $w_i, i=1...n$. For example, in Logistic Regression, using SGD, $b \ \text{or} \ w_0$ is updated with: $$...
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How can I minimize features of the trainded model?

I have real technological process, that explained with complex model (xgboost). I.e. current mass of a product (y) depends on current temperature (x1), pressure (x2) and so on. I would like to solve ...
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135 views

Using Mean Squared Error in Gradient Descent

I've recently been writing linear regression algorithms from scratch to gain an understanding of how the maths behind it works (something that was a bit of a black box beforehand), and so I got around ...
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Precision recall loss function

I've been using precision and recall as my metrics, as per keras-team/keras/pull/9393/files Sensitivity & specificity is what I want to optimise for. Every epoch I output it: ...
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Difference between MOE and Spearmint?

MOE seems to be a plain Bayesian optimization. Just curious if anyone knows about the difference.
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What is a good classification type Machine Learning toolbox for a beginner to conduct geometric optimization?

I am looking to make composite part of 2 different materials. I want to analyze many different part geometries and use ML to suggest the strongest geometric structure. To expand on this thought, I ...
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Can a GAN-like architecture be used for maximizing the value of a regression predictor?

I can't seem to convince myself why a GAN model similar to regGAN couldn't be modified to maximize a regression predictor (see the image below). By changing the loss function to the difference ...
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What does it mean to 'gradient check a few dimensions for every separate parameter'?

In lecture notes for cs231n while discussing checking analytical gradient with numerical gradient the paragraph says this: Check only few dimensions. In practice the gradients can have sizes of ...