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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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### Optimal combination of variables to minimise output

To be honest I'm not 100% sure how much this is purely a coding issue or a data science issue, but I'll take my chances. I've developed a matrix which is a mixture of various hyperparameters, the ...
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### Scheduling Production on One Machine with Changeover Costs and due dates

I'm trying to develop a solution to find a local optimum to a combination of manufacturing orders. They have a changeover cost per type, this means that the change between a type 2 order and type 3 ...
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### Using Reinforcement learning for minimisation

I would like to use reinforcement learning for the optimisation of a given function under some contraints. Take for example the following problems: ...
1 vote
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### How to comment on goodness of loss functions?

I have two loss functions $\mathcal{L}_1$ and $\mathcal{L}_2$ to train my model. The model is predominantly a classification model. Both $\mathcal{L}_1$ and $\mathcal{L}_2$ takes are two variants of ...
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### Each person gets their top, or second choice of activity over a period of 6 slots

We are running a camp for 130 children, and on 3 days they can pick different activities to do. One activities for slot 1 (45min), the other for slot 2 (another 45min), enabling them to do 6 ...
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### Bulding Deep Learning model for multiclassification case

I am soo confused i read a lot of information in forumas and still cna't get what is wrong. my data is around 500.000 rows and 32 columns. my target variables consists of 3 classes (0, 1, 2). Hyperopt ...
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### Learning the gradient descent stepsize with RL [closed]

Problem statement: I've been working on a project to accelerate the convergence of gradient descent using reinforcement learning (RL). I want to learn a policy that can map the current state of ...
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### How to prevent update a pretrained model if a model is optimized with backpropagation in Pytorch?

I use Pytorch exclusively to develop my model, and these are components in my model and how it works: A generator An encoder: a pretrained, and should not updated. A loss function. Input is passed to ...
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### Learning the "surface" of a function

Given a continuous non-convex function and assuming knowledge of all extremum points, is it possible to learn all initialization points from which performing classic GD lead to global minimum? Pure ...
1 vote
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### How to force my model heads to learn different things?

I have an Seq2Seq model that has 2 generative LM heads. I want the two heads to focus on different features/styles while decoding. The approach that I was thinking of is adding a distance cost to the ...
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1 vote
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### Which machine learning models are rational to use on NP-hard and NP-complete "theoretical" problems?

Time and time again I run into "surprising" NP-hard problems that seem naturally simpler than they are. I recently worked on a weighted graph theoretical problem where the point is to ...
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### What do we mean by optimizer.zero_grad()

This should be a simple question. But it is vague to me. What do we mean by optimizer.zero_grad(). Consider SGD as an example: $W^{t+1}= W^{t}- \lambda g_t$. Which one becomes zero for each batch. It ...
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