Tagged Questions

Theoretical questions in Neural and Evolutionary Computing

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Is current hardware adequate for neural networks ? Are there more adequate hardware? [on hold]

If you have a large neural network and you use more than 10 cores, it will be limited by the fact each core will need to read/write data that it can't access fast enough. I've read about some samsung ...
0
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0answers
22 views

Are there other names for multilayer perceptrons or multidimensional interpolants based on Kolmogorov's approximation work?

Are there other names for multilayer perceptrons that are used outside of the neural net community? At its core, multilayer perceptrons form a multidimensional interpolant of the form $$ ...
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0answers
88 views

How to define deep learning? [closed]

Ive read some articles about deep learning but I found its hard to provide a clear definition of deep learning. For me its like an intelligent feature selection method. But it seems that its not ...
1
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1answer
88 views

Theoretically, can everyday computing tasks be broken down into ones solvable by a neural network?

MIT Review recently published this article about a chip from IBM, which is more or less a Artificial neural network. Why IBM’s New Brainlike Chip May Be “Historic” | MIT Technology Review The article ...
2
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1answer
22 views

Neural Networks: Incorporate feature-specific knowledge

Has any research been done about / are people interested in incorporating prior knowledge of good high-level features into a deep neural net? I know this somewhat defeats the purpose of feature ...
4
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0answers
52 views

Greedy Backpropagation: has anyone researched it?

Backpropagation is computationally expensive. Has any research been done on a partially greedy implementation of it? Intuition: at the beginning of training, big rough learning steps can be taken, so ...
6
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3answers
182 views

Neural Networks: what's the point of learning features that don't linearly separate?

Unless I'm mistaken, deep neural networks are good for learning functions that are nonlinear in the input. In such cases, the input set is linearly inseparable, so the optimisation problem that ...
1
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3answers
205 views

Which algorithms have been proposed to learn the architecture of a deep neural network?

Yoshua Benhgio's Learning Deep Architectures for AI book mentions that we should [...] strive to develop learning algorithms that use the data to determine the depth of the final architecture. ...
2
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1answer
141 views

Using Indicator Functions as Transfer Functions for Neural Networks

Does there exist any theory (other than Cybenko's proof of the Universal Approximation Theorem with sigmoids) advocating the use of indicator functions as transfer functions for machine learning with ...
3
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0answers
105 views

Practical Implications of Kolmogorov's Result on the Universal Approximation Theorem with Neural Networks

After having read matus's beautiful answer in this thread explaining (among other things) Kolmogorov's result regarding the Universal Approximation Theorem with Neural Networks, I wonder: if just ...
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0answers
61 views

Generalization Issues with Practical Suggestions from Universal Approximation Theorem with Neural Networks

After having read matus's beautiful answer in this thread explaining (among other things) Cybenko's proof of the Universal Approximation Theorem for Neural Networks, I wonder: if we use a piecewise ...
1
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0answers
91 views

Why do people like using evolution computing techniques like GA on multi-objective optimisation?

I am new to the field of multi-objective optimisation and I try to find some books to read on this topic. Yet when I search around my library I found there are many books on using evolution computing ...
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Bound on the number of hidden nodes for multilayer perceptron with a single hidden layer

The universal approximation theorem states that: Let $\phi(·)$ be a nonconstant, bounded, and monotonically-increasing continuous function. Let $I_m$ denote the m-dimensional unit hypercube ...
4
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1answer
380 views

Parameters of energy function for TSP

[This question was initially asked here. It went unanswered so I thought I should ask it in a different community.] I am reading this paper by Hopfield et al. On page six, the authors defined the ...
4
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2answers
2k views

Universal Approximation Theorem — Neural Networks

I posted this earlier on MSE, but it was suggested that here may be a better place to ask. Universal approximation theorem states that "the standard multilayer feed-forward network with a single ...
0
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1answer
98 views

When to apply soft constraints evaluation functions in genetic algorithms?

As stated in the question, say i created a random population of 100 timetables. 1 % of these timetables are valid. Then, if I apply the soft constraints evaluation functions on the 1% valid ...
0
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1answer
299 views

To what extent is it possible to use genetic algorithms to make wind mill turbine blades more efficient?

I recently watched this video on youtube. It featured someone explaining how he used genetic algorithms to improve the efficiency of wind mill turbines by finding the optimal shape for the blades. ...
3
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1answer
438 views

Neural Networks to classify accelerometer double taps

I'm building an application for Android devices that requires it to recognize, by accelerometer data, the difference between walking noise and double tapping it. I'm trying to solve this problem using ...
4
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2answers
228 views

Selection in a genetic algorithm

I have a working genetic algorithm which uses a few genetic operators on pairs of individuals. These two individuals are selected by fitness-proportional (roulette-wheel) selection, that is, an ...
2
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2answers
326 views

Accuracy rate in neural networks

I've implemented a neural network (using the back-propagation algorithm) in a digital marketing application, where the algorithm classifies words according to quality, price and another ...
2
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1answer
149 views

Linear Function Representation of Neural Network

I have trained a neural network using MATLAB and am ready to deploy it into my software. Right now, I include it in my software by programming the structure of the neural network and the connection ...
2
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0answers
158 views

Computational power of cellular neural networks

A cellular neural network is a kind of recurrent neural network that could be thought of as a hybrid between neural nets and cellular automata. As I understand it, the classic Chua-Yang CNN is ...
3
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1answer
113 views

What is the correct name for the space of genotypes and fitness?

I'm looking for a formal definition of the space that consists of the dimensions gene 1-N and the fitness. In literature there is often the search space mentioned, but it only contains all genes. If ...
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2answers
1k views

Purpose and determining when to use hidden layers

Following up on this question... I am attempting to learn how to use and create neural networks for my research, and one point is somewhat escaping me. I realize that hidden layers are a somewhat ...
0
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1answer
461 views

What are some examples of sequential-decision tasks?

I ask this because I'm currently learning about Neural Networks as a subset of the machine learning algorithms Just trying to get some intuition on what sort of problems out there are categorized as ...
3
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0answers
60 views

Can we benefit the linear relation between $\sigma$ and best convergence speed in $(\mu,\lambda)$-ES and $(\mu+\lambda)$-ES?

$(\mu,\lambda)$-ES and $(\mu+\lambda)$-ES are two well-known Evolution Strategies. One variant of these algorithms is when we set a constant value for $\sigma$ and generate off-springs for $x$ as: $$ ...
2
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1answer
260 views

GA puzzle solver stuck at local maximum

I have a jigsaw type problem with 192 pieces which I am trying to find solutions to. I have written a GA which starts from a random allocation then 'crosses' by taking rectangular blocks from one ...
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2answers
321 views

What limits the performance of evolutionary computing techniques?

What limits the current performance of genetic algorithms and neural networks? The principles underlying these techniques, at least the popular science presentation of these principles, suggests that ...
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2answers
273 views

Genetic Algorithm convergence test functions

I was working on parallel implementation of Genetic Algorithm with MapReduce. I have found that in many papers authors are referencing OneMAX as problem they used to test scalability and convergence ...
3
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2answers
476 views

Dealing with duplicates in Genetic Algorithm

I'm using Genetic Algorithm to create a rota for home-care organisation. All the groundwork is complete, I'm getting the results, but results are not as good as expected. If I calculate fitness for ...
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2answers
269 views

Genetic algorithms

Is it theoretically possible to reconstruct the contents of a file from its id using evolutionary computing? A file in this case can be a text, image, video or audio file. The 'id' in this case, ...
14
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2answers
386 views

Quantum PAC learning

Background Functions in $AC^0$ are PAC learnable in quasipolynomial time with a classical algorithm that requires $O(2^{log(n)^{O(d)}})$ randomly chosen queries to learn a circuit of depth d [1]. If ...
21
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1answer
581 views

How much computational power fits into a cubic centimeter?

This question is a followup on the question about DNA algorithms asked by Aadita Mehra. In comments there, Joe Fitzsimmons said, in part: [T]he radius of the system must scale proportionately to ...
9
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1answer
630 views

Computational Power of Neural Networks?

Let's say we have a single-layer feed forward neural network with k inputs and one output. It calculates a function from $\lbrace 0,1\rbrace ^{n}\rightarrow\lbrace 0,1\rbrace $, it's fairly easy to ...
0
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1answer
132 views

Analysis of variables of varying numbers

i work with amino acid sequences and i want to use a selfmade model to tell me something about it, lets call it f(seq). Now i want to know the contribution of every position in the sequence onto the ...
7
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2answers
477 views

What subjects, topics does a computer science graduate need to learn to apply available machine learning frameworks, esp. SVMs

I want to teach myself enough machine learning so that I can, to begin with, understand enough to put to use available open source ML frameworks that will allow me to do things like: Go through the ...
0
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1answer
412 views

What does “number of inputs to each neuron” mean in Neural Network terms?

I am reading about a Neural Networks project that has some data like this I am new to this, and though I think I understand what a 3:1 network mean, I do not understand what number of inputs (to ...
48
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10answers
2k views

Provable statements about genetic algorithms

Genetic algorithms don't get much traction in the world of theory, but they are a reasonably well-used metaheuristic method (by metaheuristic I mean a technique that applies generically across many ...
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2answers
763 views

What is schema theory within Genetic Algorithms

I understand (some) of the workings behind schema theory in genetic algorithms, for example: *****0 would match the genome ...
9
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11answers
2k views

What are some real world applications for genetic algorithms?

What are some real world problems that have been solved using a genetic algorithm? What is the problem? What is the fitness test used to solve this problem?