Questions tagged [optimization]

For questions about implementing and improving optimization algorithms used in creating AI programs, or optimization in general.

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Do NNs suffer from lack of efficiency in network structure and suggesting training parameters?

I am working on dynamical systems using Optimal Control theory and trying to find the connection between this field and Machine Learning. Consider a simple 2-layer Neural Network (NN) where the ...
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Are there more optimization methods like GAE for PPO [closed]

I posted about this earlier, but got the suggestion to separate the questions. I'm currently trying to "solve" the OpenAI gym "Humanoid" environment. To improve the training ...
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What method to use when optimizing an array of data

Say I have an array of data, where each element describes a shape made of points, in vector form (each vector has several hundred dimensions). Each element also has a rating that gets higher, the ...
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How to create a loss function that penalizes duplicate indices in the output tensor?

We're working on a sequence-to-sequence problem using pytorch, and are using cross-entropy to calculate the loss when comparing the output sequence to the target sequence. This works fine and ...
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How does MAML inner loop optimization works?

I started to learn meta-learning, reading the MAML paper https://arxiv.org/pdf/1703.03400.pdf In the inner loop, I am calculating adapted parameters for each task, I will be doing multiple steps of ...
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Resolving Derivation Discrepancies for Differentiating through Optimization Paths

I'm reading the paper "Optimizing Millions of Hyperparameters by Implicit Differentiation". The key contribution of the paper is to show that you can replace optimizing through the ...
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How to generate data lying in the union of different hyperplanes using a VAE

I know that a way to possibly encode prior knowledge into neural networks training is by using differentiable optimization layers (paper). I'm in the following situation, and I'm wondering if it could ...
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Structured policies in dynamic programming: solving a toy example

I am trying to solve a dynamic programming toy example. Here is the prompt: imagine you arrive in a new city for $N$ days and every night need to pick a restaurant to get dinner at. The qualities of ...
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What is the use of utilizing q-Gaussian mutation operators in evolutionary algorithms?

There are great numbers of evolutionary algorithms for optimization of engineering problems which each of them gives its own objective function value in a defined problem. Using the q-Gaussian ...
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Best algorithm for the Word Ladder puzzle

What would be the best performing algorithm to solve the Word Ladder problem, in terms of guaranteed finding of the shortest solution in the shortest possible time? Is it BFS, DFS, A*, IDA* or another ...
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Using neural network to find the most similar items

I am trying to design a multilayer neural network that based on item features will return x amount of the most similar items from the dataset. My current neural network takes features of 2 items, and ...
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How can TRPO with constrained form allow larger update step?

There are two optimization forms of TRPO. One is that: \begin{equation}\max\limits_{\theta}[L_{\theta_{old}}(\theta) - CD^{max}_{KL}(\theta_{old}, \theta)]\end{equation} where $C = \frac{4\epsilon\...
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Why does my regression-NN completely fail to predict some points?

I would like to train a NN in order to approximate an unknown function $y = f(x_1,x_2)$. I have a lot of measurements $y = [y_1,\dots,y_K]$ (with K that could be in the range of 10-100 thousands) ...
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Additional Optimizations for Convolutional Models On Inferencing

I am aware of several ways to optimize a convolutional (or any) model after training to make inferencing quicker. I am currently implementing BatchNormalization Folding and removing Dropout layers ...
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What does it mean by Generalization? [closed]

Towards Theoretically Understanding Why SGD Generalizes Better Than ADAM in Deep Learning What does it mean by Generalization in this article?
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Does Stochastic Gradient Descent "Work" on (Some) Non-Convex Functions?

As we know, there has been a lot of work and research done to demonstrate that the Gradient Descent Algorithm can converge on (deterministic) convex, differentiable and Lipschitz Continuous functions :...
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Are Problems in AI Usually "Ill Posed"?

I was reading the following link (https://en.wikipedia.org/wiki/Well-posed_problem) on "Well Posed Problems". Supposedly, if a problem is "Well Posed", it must meet the following ...
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Are Bayesian Optimization Methods Better Suited Noisy Optimization Problems?

We know that in many applied contexts (e.g. Machine Learning, Loss Functions for Neural Networks), the functions we are trying to optimize are "noisy" by definition (unlike in the classical ...
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Can we compute probability of sample in R1-Natural Evolution Strategy with linear time and space?

In R1-NES, the sample is drawn from a multivariate normal distribution, $\vec{\theta} \sim \mathcal{N}\left( \vec{\mu},\mathbf{\Sigma} \right)$, with the covariance matrix is parametrized by $s$ and a ...
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Effects of ReLU Activation on Convexity of Loss Functions

I have heard the following argument being made regarding Neural Networks: A Neural Network is a composition of several Activation Functions Sigmoid Activation Functions are Non-Convex Functions The ...
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Can we use Stochastic Gradient Descent and its derivatives for geographical researches?

I am a freshman AI engineering student actually working on a project where I have to analyze a big amount of geographical data to find out an optimal position of latitude and altitude for a specific ...
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How to calculate the gradient (or derivative) of y = f(x) of y w.r.t x where y represents the order statistics divided by median of x?

How to calculate the gradient (or derivative) of y = f(x) of y w.r.t x where y represents the order statistics divided by median of x? For instance x is ...
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What I Should Do to Reduce Solution Size for Simulated Annealing Algorithm?

I am trying to find the best solution for radar placement problem with using multi objective simulated annealing algorithm. So there is an area (in real map) and I want to put minimum count of radar ...
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Why does the schema theorem of genetic algorithms hold?

I have been reading about the Schema Theorem - one of the first theorems from the field of evolutionary computing and genetic algorithms, largely responsible for justifying the use of genetic ...
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1 vote
1 answer
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Does reaching the global optima guarantee good performance in a task?

It is to my understanding that, in deep learning, we are essentially trying to minimize the loss function that we have defined and reach its global optima through some form of optimization technique. ...
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Limit of momentum update equation

I am self-studying on optimization algorithm on https://d2l.ai/chapter_optimization/momentum.html and couldn't get my head around some derivation: Instead of the standard gradient descent update ...
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Is it possible to find a good neural network structure without training it? [duplicate]

Neural networks consist of so many parameters. Researchers could create as many possible neural networks as they wish. So I want to ask a general question. Could we devise an evolutionary algorithm ...
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a loss for binary step function data

I have some data with ground truth that looks like a binary step function, where part of it is 0 and part is one. An example for the GT can be like ...
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Why is there a Hessian diagonal approximation? And when can we use it?

This topic has been introduced in "Pattern Recognition and Machine Learning, Bishop, 2006", section 5.4.1. I am a bit confused about this method and I have two questions. Why this method ...
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How to smooth a cost function?

I have a combinatorial optimization problem whose loss function is really unsmooth now. Without specifying my problem in detail, I was wondering if there are some general methods/steps I can apply to ...
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2 votes
1 answer
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How do I use machine learning to create an optimization algorithm?

Let's say that I want to create an optimization algorithm, which is supposed to find an optimum value for a given objective function. Creating an optimization algorithm to explore through the search ...
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Is there a way to adapt Particle Swarm Optimization to an incremental/online learning setting?

As stated in the title, is there a way to adapt PSO to an online scenario where new data samples arrive continuously? In more detail: suppose that I have a classifier with several parameters for which ...
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2 votes
1 answer
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Are Genetic Algorithms suitable for a problem with a non-unique optimal solution?

I was wondering if a genetic algorithm is useful if the optimization problem has several optimal solutions. My thought was that I should not use it since when combining two members of a population who ...
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Why is gradient descent used over the conjugate gradient method?

Based on some preliminary research, the conjugate gradient method is almost exactly the same as gradient descent, except the search direction must be orthogonal to the previous step. From what I've ...
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Multi-armed Bandit in optimization on graph edges selection

I have the problem, which I described below. I wonder if there exists a class of multi-armed bandit approaches that is related to it. I am working on computer networking optimization. In the simplest ...
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Why are optimization algorithms for deep learning so simple?

From my knowledge, the most used optimizer in practice is Adam, which in essence is just mini-batch gradient descent with momentum to combat getting stuck in saddle points and with some damping to ...
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1 vote
1 answer
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What is uncentered variance and how it becomes equal to mean square in Adam?

I have been reading about Adam and AdamW (Here). The author mentioned that in "uncentered variance" we don't consider subtracting mean In this statement, the author is talking about ...
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How do you decide that you have tested enough hyper-parameter combinations for a specific neural network architecture?

How do you decide that you have tested enough hyper-parameter combinations for a specific neural network architecture to discard it and move on to a new model? Do you have a structured (generic) ...
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Compare the efficiency of a trained ML model with a non-learning-based method for solving the same problem

If a certain task T is solved by a non-learning-based method A (let's say, an optimization-based approach). We now train a machine learning model B (let's say a neural network) on the same task. What ...
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How do I solve a minimization problem with Q-learning learning?

I am trying to learn reinforcement learning by myself and so I have a lot of doubts. In particular, I am investigating how to use Q-learning in order to solve minimization problems. For example, ...
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1 answer
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Which class of functions are quite complicated in deep learning?

Deep learning is a field in which we need neural networks that are deep enough to carry on our task. The important fucntions in deep neural networks can be classified in to three classes: activation ...
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1 answer
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Do gradient-based algorithms deal with the flat regions with desired points?

I am studying a chapter named Numerical Computation of a deep learning book. Afaik, it does not deal with flat regions with desired points. For example, let us consider a function whose local/global ...
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How many directions of gradients exist for a function in higher dimensional space?

Gradients are used in optimization algorithms. Based on the values of gradients, we generally update the weights of a neural network. It is known that gradients have a direction and the direction ...
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What all does the gradient tells us other than the direction to move parameters?

Gradients are used in optimization algorithms. I know that a gradient gives us information about the direction in which one needs to update the weights of a neural network. We need to travel in the ...
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Reason for relaxing limit in derivative in this context?

Consider the following paragraph from NUMERICAL COMPUTATION of the deep learning book.. Suppose we have a function $y = f(x)$, where both $x$ and $y$ are real numbers. The derivative of this function ...
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What is meant by non-convergent limit cycles?

Limit cycle is a closed curve that is isolated i.e., no other closed curve near to it. You can read the following paragraph from here If there is (such) a closed curve, the nearby trajectories must ...
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Cover a surface with smaller predefined objects

I'm trying to make a program that takes a surface designed by the user, and different 3D geometries from a dataset as inputs and gives a good approximation of the surface using only the objects found ...
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3 votes
1 answer
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What are the necessary mathematical properties to be a loss function in gradient based optimizations?

Loss functions are used in training neural networks. I am interested in knowing the mathematical properties that are necessary for a loss function to participate in gradient descent optimization. I ...
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What does the lambda parameter in the paper "Interpretable Explanations of Black Boxes by Meaningful Perturbation" do?

I do not understand the purpose of the $\lambda$ parameter in equation 3 of the paper Interpretable Explanations of Black Boxes by Meaningful Perturbation. $$m^{*}=\underset{m \in[0,1]^{\Lambda}}{\...
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Is pruning only applicable to convolutional neural networks?

This article talks about pruning in the context of convolutional neural networks: One of the first methods of pruning is pruning entire convolutional filters. Using an L1 norm of the weight of all ...
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