Questions tagged [neural-networks]

Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

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532 views

Text data comparison

Okay lets say i have two data structures . two phone data for example containing their Name and spec ( cpu , ram , display etc ) . I want to check if these two phones are the same or not . Their names ...
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121 views

Teaching perceptrons colors? [closed]

I am learning about artificial neural networks and I've decided to go with perceptrons. I already made a sample program that can learn based on the learning data, but when I tried to make it recognize ...
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63 views

How to calculate activation of hidden nodes in a recurrent neural network?

Usually, when I program recurrent neural networks, I use a loop for each neuron to figure out it's state. What I realized with this is that in this case, no neuron gets any feedback. They just pump ...
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303 views

How can neural networks learn to create new things (sentences for example)? [closed]

I have already taken a college course at my uni on machine learning where we implemented all the basic ML programs: linear regression, logistic regression, basic neural network with logistic ...
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179 views

Computer vision training procedures: SVM/AdaBoost vs Neural Networks

With SVM, adaboost or similar alogrithms, image training sets must be cropped with specific constraints (keep image cropping ratio the same, have object tightly cropped, same resolution) In general, ...
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231 views

Hessian-Free instead of LSTM for Recurrent Net Machine Translation

Last year, Ilya Sutskever and collaborators came out with a paper about a recurrent LSTM net that learns sequence to sequence mappings for machine translation. It's somewhat surprising that the ...
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How do NEF networks differ from ANNs in applications?

As I've described here, the Neural Engineering Framework (NEF) has some functional similarities to Artificial Neural Networks (ANNs). Naturally, there is also an overlap in applications between NEF-...
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909 views

Improving MSE as fitness function for a genetic algorithm

I am implementing an autoencoder neural network in matlab, the weights of which are being optimised by a genetic algorithm. At the moment I am working on the first layer, trying to get an improved ...
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What types of images should I use for negative examples in a classification problem? [duplicate]

I am doing a project to recognize a kind of leaf using ANNs with Emgu CV in C#. My project is to get frames from camera then present them to the ANN and have the ANN tell me if that frame contain a ...
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609 views

How to determine if a state is a fixed point in a Hopfield network?

I have been reading a lot and I am still unsure of how to determine this. Let's say I have an initial binary state vector (1, 1, 1). How would I go about determining whether (1, 1, 1) is a fixed point ...
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58 views

Convolutional Neural Network with constant kernels

I'm starting to learn about CNNs, and I have this question that I haven't been able to answer. Sorry if it is too basic. I know that in a CNN, the network learns to extract relevant features of ...
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134 views

Training a neural net without labels in Reinforcement Learning

I am trying to dig further into machine learning and I am making a program to play a game as a start. I have created a game that is based on the mobile game Flappy Bird and can be generalized to the ...
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419 views

Why is an Artificial Neural Network with high accuracy score giving poor results?

I implemented my ANN using SKlearn module's class MLPClassifier. Fitting it on some data and testing it on a very specific subset of said training data, it gives a score of 1.0, but actually using the ...
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305 views

Genetic Algorithm Neural Network- Inputs for evolving creatures

I made a GANN program for evolving creatures. The genes that get put into the GA for each ind are the weights that go into the neural net for each creature. The NN is a basic 1 hidden layer NN with ...
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321 views

Trying to Classify MNIST where pixels are shuffled with a seed.Why is it not working?

I transformed the MNIST dataset as follows:(X (70000 x 784) is the training matrix) ...
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1answer
288 views

Genetic algorithm neural networks converges, but suddenly stops

I'm trying to create a genetic algorithm to train neural networks (because I'm to bad at back-propagation), and it works well until generation 18, where the loss stops to decrease and gets constant. ...
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553 views

Methods to prevent overfitting

I am aware of three approaches to prevent over-fitting of data when trying to model it on a neural net. The first two approaches I know suggest to train on more data and employ bootstrap aggregating. ...
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234 views

Comparative study between Deep neural nets and Bayesian Networks

Is there any comparative study that showcases the powers of Bayesian Networks and Deep learning in their respective favorable setup and how they compare? I tried to go through blogs but couldn't find ...
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61 views

What is the difference of temporal dynamics in RNNs and the NEF

Both spiking neural networks created with the Neural Engineering Framework (NEF) and Recurrent Neural Networks (RNNs) can be connected recurrently to exhibit neural dynamics. What is the difference ...
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ANN architectures that perform faster/efficient prediction over time?

ANN when compared to biological neural systems have this common concept of reducing error in prediction over time (training) and becoming more good at predicting correctly. But there is one behavior ...
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1answer
9 views

Intersection of two generative neural networks

I have an input x (a word), and I have a neural network Y such that Y(x), which is an image always satisfies F(x), and another neural network Z such that Z(x), which is also an image, always satisfies ...
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47 views

How does image reconstruction take place in neural network?

I am reading through and thinking about how neural network works and have been reading about convolutional neural networks (CNN). I am particularly interested in image filtering (or enhancing) using ...
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24 views

How to initialize the first h in an RNN?

Take a Vanilla RNN represented by the function $h_t = f(h_{t-1}, x_t)$, how do you determine $h_0$? Edit: This answer over on the stats page has helped.
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66 views

Is it possible to detect cats from dogs in image with single layer perceptron?

I want to make a simple application that input is an image and output must be 0 if image is dog and 1 if image is cat. Is it possible to detect cats from dogs in image with single layer perceptron?
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809 views

Single Layer Perceptron vs Multi Layer Perceptron

Why the single layer perceptron has a linear activation function while the Multi Layer Perceptron has a non-linear activation function ? What is the potential of the Multi Layer Perceptron respect of ...
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1answer
79 views

How many neurons is needed for UAT to hold?

The universal approximation theorem states that a feed-forward network with a single hidden layer containing a finite number of neurons can approximate continuous functions on compact subsets of Rn, ...
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94 views

Correct cost function of multi classification problem using neural network?

I am going through machine learning course on coursera. While going through the section on neural networks I came across the cost function for multi - classification problem using neural networks ( ...
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101 views

MLP and backpropagation limitations?

I have heard a colleague of mine giving the following statements to a student, but I am not quite sure if he is right. The statements were about Multi Layer Perceptron and the Backpropagation ...
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57 views

textbook on the human brain for computer scientists/machine learning professionals?

The human brain as studied by neuroscientists, or neurobiologists, generally focus on what I would call implementation details (how specifically does information transport between synaptic channels, ...
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152 views

Matrix multiplication in recurrent neural networks

I was looking at a tutorial for recurrent neural networks in Python, and I have a question in regards to multiplying matrices of different sizes. Specifically, why does S[t] have 100 elements in it? ...
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1answer
670 views

Crossover operator in genetic algorithms in Neural Networks

I am developing a neural network that is trained using a genetic algorithm. The neural network is a multilayer perceptron using $\tanh$ as its activation function. Currently, the genotype of the ...
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1answer
152 views

Dimension of 1x1 convolution output

I am having a hard time understanding maths behind a 1x1 convolution and how is it actually performed. Assuming that I have a 6x6x32 input to my 1x1xK convolution layer similar to the one presented ...
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2k views

How to feed videos to a neural network

I have been coding and testing Neural Networks for a while but as of now I have only used IMAGE datasets. (i.e. I have M training images and N testing images). Some datasets are video datasets. The ...
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56 views

Neural network to detect connected parameters

I have a list of configurations. Each configuration contains a number of parameters. The parameters have the same name but sometimes different values. I now want a neural network, that can detect, ...
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50 views

create new data from trained ANN

I use a very simple neural network to make classification between classes. Once my ANN is trained I'm able to present new and unknown data, and get a good classification. Is there a simple way to ...
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334 views

what does activation function become in spiking neural network?

In common ANN, nodes are based on a activation function, like a sigmoid or tanh or ReLU. Updates of synaptic weight are based on the derivative of this function. When node are represented with ...
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85 views

Training a neural network by classifying its own output

The usual way one trains a neural network is to give it some input and provide the correct classification. But what about letting the neural network produce its own inputs, and then classifying those?...
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66 views

Which class of neural network problem is this?

I have managed to describe a problem in quantum computing as the optimization of a function f(graph,vector), over graphs and real vectors. For a given graph, I can ...
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928 views

Backpropagation in multiple output neural networks

I want to understand how the backpropagation algorithm would work on a neural network with multiple outputs. More specifically, I have a network with 21 binary (0/1) outputs and I want to minimize ...
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1answer
637 views

Big number of false positives in binary classification

I am fine-tuning the inception-v3 network(pretrained on ILSVRC) of tensorflow in a binary classification problem. I want to recognize if an image contains food or not. My dataset is imbalanced, the ...
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340 views

Architecture of a chatbot - how to organize and fetch possibilities? [closed]

I'm building a chatbot that would respond to text messages. Let's say that my chatbot works for customers of an internet provider and it can respond to the following things: Problems: About payment;...
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1answer
509 views

How different is the working of SNN (Spiking Neural Network) as compared to a real Neuron System in biological systems?

Assuming its one step closer to realism as compared to ANN, DNNs and other Neural Network models, what are the primary differences between a real neuron system and SNN?
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486 views

Backpropagation for 'Classification' Neural Network

Question: How does one formulate a back propagation algorithm (either batch, gradient, anything that works) for a neural net, playing a game of Tic Tac Toe? (Java is being utilized) Scenario: There ...
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477 views

Tackling overlearning neural network issue

I'm trying to train a neural network using this sort of data (for a homework): Number of Features : 42, Target data : 0 or 1, Number of Samples : 111 Individuals ( 69 Cases + 42 Controls ) However i'...
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198 views

How to make a Neural network understand that multiple inputs are related to the same entity?

Neural networks can have multiple inputs. But some times two or more of these inputs can often be related to a single entity. E.g : Height and weight of a person to predict the probability of disease ...
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Using the features embedding of the output from a transformers to represent probabilities of categorical data

I was considering using a transformer, on input data which can be represented as an embedding, so I can use the attention mechanism in the transformer architecture. As my data is of variable input and ...
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33 views

Delta rule for binary step function

I have a question of understanding about the Delta Rule: $\Delta w_i = (y - \hat{y}) \times x_i$ Why does $x$ have to be multiplied again after the difference? If the input is $0$, the product of $w$...
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1answer
20 views

Performing semantic segmentation on images on word documents

At the moment, I'm making a comparative analysis between two different neural networks for a class. The problem they are both trying to solve is detecting tables on documents. Below is an example of ...
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How to find clusters of a set of points in n-dimensional space that are furthest from unwanted points

I have a list of 25 points and their coordinates in a 512-dimensional space. I have 8 target points and 17 points I need to avoid (the 17 points to avoid also have differences in priority of how ...
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Artificial Neuron: question on definition and inhibited signals

This is an artificial neuron of a NOT function from Rojas's ML book. I have a question on it's behavior. It's my understanding that the neuron produces a signal if it's inputs $x_1$ summed are $\geq ...

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