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Questions tagged [optimization]

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

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Estimating Baselines using ALS

I am trying to figure out how ALS works when minimizing the following formula: $\\ \\$ $\text{min}_{\lbrace b_u,b_i \rbrace} \sum_{(u,i)\in \mathcal{K}} (r_{ui} - \bar{r} - b_u - b_i )^2 + \lambda_{...
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27 views

How to properly optimize shared network between actor and critic?

I'm building an actor-critic reinforcment learning algorithm to solve environments. I want to use a single encoder to find representation of my environment. When I share the encoder with the actor ...
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2answers
48 views

How can the A* algorithm be optimized?

How can the A* algorithm be optimized? Any references that shows the optimization of A* algorithm are also appreciated.
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1answer
50 views

How can we reach global optimum?

Gradient descent can get stuck into local optimum. Which techniques are there to reach global optimum?
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1answer
91 views

Why isn't the reverse KL divergence commonly used in supervised learning?

Forward KL Divergence (also known as cross entropy loss) is a standard loss function in supervised learning problems. I understand why it is so: matching a known a trained distribution to a known ...
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33 views

Double Convolution Layers in Yolov3

Lately, I have been working on yolov3 and have been trying to train it on x-ray images to detect a fracture. However, I have decided that I would want to increase the number of convolution layers for ...
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1answer
44 views

How could we solve the TSP using an hill-climbing approach?

How could we solve the TSP using an hill-climbing approach?
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1answer
100 views

When should we use algorithms like Adam as opposed to SGD?

As far as I know, Stochastic Gradient Descent is an optimization algorithm which belongs to the the category of algorithms where hyper-parameters have to be defined beforehand. They are useful in many ...
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27 views

Why is my generator loss function increasing with iterations?

I'm trying to train a DC-GAN on CIFAR-10 Dataset. I'm using Binary Cross Entropy as my loss function for both discriminator and generator (appended with non-trainable discriminator). If I train using ...
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1answer
154 views

How can we use linear programming to solve an MDP?

Apparently, we can solve an MDP (that is, we can find the optimal policy for a given MDP) using a linear programming formulation. What's the basic idea behind this approach? I think you should start ...
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1answer
39 views

Recent Python Packages for Random Search Optimization

Which python packages do you recommend on random search optimization to use? Is there any recent and good one (better than the one in Sci-kit)?
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What is the purpose of the new neurons in the constrained neural network?

I would like to train a constrained neural network. I found a paper on this: https://papers.nips.cc/paper/4-constrained-differential-optimization.pdf. However, I don't really understand how to change ...
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Feature selection optimization and hyperparameters optimization for one model

Question is purely theoretical. I am desiging a machine learning model for classification purposes. I am using GridSearch optimization method to select best hyperparameters and I have written ...
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1answer
37 views

Training an artificial neural network using PSO

Hi guys I've been studying such combination where the idea is to replace the classic descendant gradient for an algorithm that is less sensitive to local optimum, so the PSO tries to select the best ...
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1answer
37 views

Which local minima to choose according to the shape of the error surface?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Considering the ...
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1answer
32 views

Python Packages for Recent Optimization Methods

I want to try and compare different optimization methods in some datasets. I know that in scikit-learn there are some corresponding functions for grid and random search optimizations. However, I also ...
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2answers
54 views

Could error surface shape be useful to detect which local minima is better for generalization?

The following plot shows error function output based on system weights. Two equal local minima are shown in green pointers. Note that the red dots are not related to the question. Does the right one ...
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0answers
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Any guidance on learning rate / batch size for noisy data (high Bayes error rate)?

Is there any guidance available for training on very noisy data, when Bayes error rate (lowest possible error rate for any classifier) is high? For example, I wonder if deliberately (not due to memory ...
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Is a very powerful oracle sufficient to trigger the AI singularity?

Lets say we have a oracle $S$ that, given any function $F$ and desired output $y$, can find an input to $x$ that causes $F$ to output $y$ if it exists, or otherwise returns nil. I.e.: $$S(F, y) = x \...
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Feature visualization on neural networks which are not for classification

Feature visualization allows to better understand neural networks by generating images that maximize the activation of a specific neuron, and therefore understand what are the abstract features that ...
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1answer
43 views

Method to check goodness of combinatorial optimization algorithm implementation

How do I check which algorithm solves my problem best? Given a optimaization problem, I apply different well known optimization algorithms (genetic algorithm, simulated annealing, ant colony etc.) to ...
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1answer
63 views

time complexity for shortest path: Ant colony optimization algorithms vs. Classical routing algorithms

For the shortest path problem in a graph, which type of algorithms have better average time complexity? Ant colony optimization algorithms vs. Classical routing algorithms? In general, can we compare ...
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1answer
279 views

RNN LSTM not converging with Adam

I am trying to train a RNN with text from wikipedia but I having having trouble getting the RNN to converge. I have tried increasing the batch size but it doesn't seem to be helping. All data is one ...
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2answers
48 views

How can a specific connectivity pattern be stored in an optimally compact representation?

I am interested in optimizing the memory capacity of an AGI. Given a specific complex input an AI can create a simplified model. This is a problem that can be solved using sparse coding [1]. However, ...
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Classical Internet routing vs. Swarm routing (such as Ant routing)?

Is it possible to mention the drawbacks/advantages of Swarm routing (such as Ant routing etc) in comparison with classical routing algorithms in communication networks in a general view? In other ...
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2answers
94 views

Continuous Domains vs. Discrete Combinatorial Optimization

According to this website: http://yarpiz.com/67/ypea104-acor (in the website it is mentioned that it is a project aiming to be a resource of academic and professional scientific source codes and ...
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2answers
112 views

When should I use simulated annealing as opposed to a genetic algorithm?

What kind of problems is simulated annealing better suited for compared to genetic algorithms? From my experience, genetic algorithms seem to perform better than simulated annealing for most problems....
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1answer
41 views

Neural Network Optimizers in Reinforcement Learning non-well behaved environments

https://stackoverflow.com/questions/36162180/gradient-descent-vs-adagrad-vs-momentum-in-tensorflow Here, the nice gifs explain how different algorithms approach towards the root. Unfortunately, the ...
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1answer
68 views

Genetic Algorithm vs Particle Swarm Optimization

Which one gives better optimization results? Genetic Algorithm or Particle Swarm Optimization? Can I use them for online tuning problems? Thanks in advance!
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1answer
78 views

Why does hill climbing algorithm only produce a local maximum?

Apparently, the hill climbing algorithm just produces a local maximum, and not necessarily a global optimum. It's stuck on a local maximum. Why does hill climbing algorithm only produce a local ...
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0answers
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What does it essentially mean if the neural network has convex error surface?

Suppose if I am building a Linear Regression model with one fully connected layer and a sigmoid with minimizing mean squared error as objective. Why would the error surface be convex? Does finding ...
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1answer
70 views

What is the basic purpose of local search methods?

I read about the hill climbing algorithms, the simulating annealing algorithm, but I am confused. What is the basic purpose of local search methods?
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1answer
39 views

Reinforcement Learning to Grouped Scheduling Optimisation Problem

I am not sure the name of this kind of problem, but anyway, the situation is as below. Assign teachers into Groups and consider on each of their workload, availability etc. There are some other soft/...
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2answers
4k views

What are the limitations of the hill climbing algorithm and how to overcome them?

What are the limitations of the hill climbing algorithm? How can we overcome these limitations?
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4answers
297 views

What is the actual learning algorithm: back-propagation or gradient descent?

What is the actual learning algorithm: back-propagation or gradient descent (or, in general, the optimization algorithm)? I am reading through chapter 8 of Parallel Distributed Processing hand book ...
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1answer
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Way to control movement and coverage in an embedded AI cleaning system?

There's the need to design a horizontal plane cleaning system that is controlled by positioning servos. Two in two of three floor rollers and three in the x, y, and z positioning of a wiping device. ...
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2answers
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How much can the addition of new features improve the performance?

How much can the addition of new features improve the performance of the model during the optimization process? Let's say I have a total of 10 features. Suppose I start the optimisation process using ...
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1answer
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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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1answer
143 views

What are the advantages and disadvantages of using LISP for constraint satisfaction in 3D space

We are currently working on developing a 3D modeling software that allows designers to set spatial constraints to models. The computer then should generate a 3D mesh conforming to these constraints. ...
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1answer
129 views

How to optimize a function using a genetic algorithm?

I recently learned about Genetic algorithms and I solved the 8 queens problem using a genetic algorithm but I don't know how to optimize any functions using a genetic algorithm. I want a guide on how ...
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1answer
40 views

What is the difference between the study of Evolutionary algorithm vs. Optimization?

I have a course named "Evolutionary Algorithm". But, our teacher is always mentioning the word "Optimization" in his lectures. I am confused. Is he actually teaching Optimization? If yes, why is the ...
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1answer
84 views

Is a calculus or ML approach to varying learning rate as a function of loss and epoch been investigated?

Many have examined the idea of modifying learning rate at discrete times during the training of an artificial network using conventional back propagation. The goals of such work have been a balance ...
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1answer
2k views

Loss jumps abruptly when I decay the learning rate with Adam optimizer in PyTorch

I'm training an auto-encoder network with Adam optimizer (with amsgrad=True) and ...
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1answer
60 views

Genetic Algorithms: Trade-off between time and variance with regards to fitness function

I'm developing an AI to play a card game with a genetic algorithm. Initially, I will evaluate it against a player that plays randomly, so there will naturally be a lot of variance in the results. I ...
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1answer
32 views

How do I compute log-likelihood for training set in supervised learning?

I am building a supervised learning model and I wish to compute the log-likelihood for the training set at the point of the minimum validation error. Initially, I was computing the sum of all the ...
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0answers
30 views

Optimization step in Apprenticeship Learning via Inverse Reinforcement Learning

Why the optimization step of the algorithm a quadratic program? [See: Apprenticeship Learning via Inverse Reinforcement Learning; page 3] Isn't the objective function linear? Why don't we treat ...
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1answer
254 views

Quiescence search

Games like checkers have compulsory moves. In checkers for instance, if there's a jump available a player must take it over any non-jumping move. My question is, if jumps are compulsory will there ...
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2answers
130 views

Is an evaluation function as good as an optimization function

I have been so for self-learning basic A.I concepts and would like to know if having a really good evaluation function as good as any of alpha-beta pruning optimization functions such as killer moves, ...
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1answer
37 views

Maximizing or Minimizing in Trust Region Policy Optimization?

I happened to discover that the v1 (19 Feb 2015) and the v5 (20 Apr 2017) versions of TRPO papers have two different conclusions. The Equation (15) in v1 is $\min_\theta$ while the Equation (14) in v2 ...
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1answer
114 views

Gradient of boltzmann policy over reward function

I'm struggling with an inverse reinforcement learning problem which seems to appear quite often around the literature, yet I can't find any resources explaining it. The problem is that of calculating ...