Neural networks traditionally refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks (ANN), which are composed of artificial neurons or nodes - programming constructs that mimic the properties of biological neurons. ANN are ...

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What is maxout in neural network?

Anyone can explain what does maxout layer do in neural network? How to perform it? What does it different to normal activation function? I try to read the 2013 "Maxout Network" paper by Goodfellow et ...
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20 views

How to standardize text data for training Neural Networks?

I want to train neural network with text data(natural language) as input for classification purpose. One way for standardizing text data for neural network is to use N-GRAM/SKIP-GRAM representation ...
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23 views

ANNs and mixed data-type problems

I did some research but I'm not quite sure if ANNs, more precisely MLPs, are able to handle mixed data-types (e.g ordinal and metric scaled variables) like in the German Credit data set without ...
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5 views

XOR backpropagation convergence

I've implemented 3 supervised training algorithms: rprop, online- and batch backprop with momentum. I have the simple XOR test, and I measured how many times they converge out of N iterations. My ...
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103 views

Deep learning algorithm

What's the difference between deep belief network and deep convex network?
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10 views

pybrain NN for classification

I have created my own NN to identify properly handwritten letters from a bunch of records. I used ClassificationDataSet with data downaloded from ...
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8 views

How can recurrent neural networks be used for sequence classification?

RNN can be used for prediction, or sequence to sequence mapping. But how can RNN be used for classification? I mean, we give a whole sequence one label.
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47 views

Types of artificial intelligence with good results [on hold]

I have been looking into artificial intelligence for some time now. I am wondering what branches are still in active research and have some good/interesting results. The two that I have looked in so ...
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5 views

ImageNet: what does top-five error means?

One of the evaluation method for ImageNet Competition (classify 1,000 categories images) is top-5 error, what does that mean? See: http://www.image-net.org/challenges/LSVRC/
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6 views

Manually Calculating Tan Sigmoid & Softmax

I'm putting together an app for my dissertation which will take a set of 34 inputs and give back a diagnosis. I've built the ANN in Matlab and trained and tested it. I am then taking the weights and ...
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15 views

Building a single-layer neural network for number recognition

I'm trying to create a very simple neural net in Javascript for a school project. The goal is to have the net identify a number drawn by the user on a square grid. Currently the size of this grid is ...
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18 views

One-class SVM vs NN with backprop… Or is there something better?

I'm pretty new to unary classification, so I've been playing around with different approaches to one-class document classification in Python. NN seemed promising at first, but has some undesirable ...
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12 views

The neural networks that are the best at invariance

There is a great number of types of neural networks. Some are better at handling invariance, some are worse, and some are not capable of it at all. I don't know any except the ASSOM, and the broad ...
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10 views

Can you take a DNN that was trained without regularization, and continue training it with regularization?

If I've trained a DNN with out any regularization methods (e.g. weight decay, dropout etc.) and reached a good training error, can I somehow take that learned net and fine tune it with regularization? ...
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number of feature maps in convolutional neural networks

When learning convolutional neural network, I have questions regarding the following figure. 1) C1 in the layer 1 has 6 feature maps, does that mean there are six convolutional kernals? Each ...
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11 views

Convolutional neural network for time series?

I would like to know if there exist a code to train a convolutional neural net to do time-series classification. I have seen some recent papers ...
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17 views

How to find formula of the neural network?

I have created a neural network in R using the neuralnet package. And using the plot.nn function, i have got the plot of the neural network. But I want to know the formula of the network, ie what is ...
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8 views

Is it possible to train a Convolutional Neural Network with distributions as supervised information

Recently I am working on a project which aims at training a convolutional neural network with some distributions as supervised information instead of discrete labels. I think I can learn some useful ...
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11 views

Designing an ANN for indoor localization

I'm working on ANN for indoor localization, the input to the network is a vector of Received Signal Strengths (RSSs), and the output is the (x,y) coordinates. I have two designs, the first one has 14 ...
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11 views

Hopfield Neural Network only for Content Addressable Memories?

I'm beggining to study some Neural Networks and i just came across Hopfield model. I'm a little puzzled about its use: is it only "limited" to content adressable memories? is content adressable memory ...
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9 views

positive and negative sample count for ConvNets

I have been trying to set up a ConvNet to classify some data. This data should be classified to either 1 (being what I need to get from the image) and 0 for everything that is irrelevant. I have ...
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35 views

How many data do you need for a convolutional neural network?

If I have a convolutional neural network (CNN), which has about 1,000,000 parameters, how many training data is needed (assume I am doing stochastic gradient descent)? Is there any rule of thumb? ...
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20 views

Neural language model training - stochastic vs batch

Dealing with a very basic neural language model: 3 words of context, vector size 100, one hidden layer size 200, vocabulary size 1000, predicting the next word with a softmax output layer. Previously ...
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1answer
28 views

Questions about Q-Learning using Neural Networks

I have implemented Q-Learning as described in, http://web.cs.swarthmore.edu/~meeden/cs81/s12/papers/MarkStevePaper.pdf In order to approx. Q(S,A) I use a neural network structure like the following, ...
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16 views

How do I view “error correct learning” in ANN as an optimal control problem?

There is a lot of material out there for the gradient descent method used in ANN but no body makes it clear how this is an optimization problem or brush it off as extraneously info. Can someone make ...
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9 views

Artificial Neuron Activation

if an activation Function has an Non-Zero output for an input of zero f(0) != 0, does that, strictly speaking, mean that the corresponding neuron would have to output a value in every time step of the ...
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75 views

Is a neural network the only way to learn input/output relation?

first question here but hopefully you can help. Firstly I'm a web programmer by trade but I've touched a bit of neural networks in my university days. I've got a project to do with predicting the ...
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13 views

pybrain LSTM layer buffer variables

In pybrain LSTM layer there are these buffer that are used to store values. ...
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11 views

Full batch backpropagation implementation

I am trying to wrap my head around using batch backprop in a neural network. I have a very code-oriented mind, and I'm trying to figure out whether it's possible to parallelize the full batch ...
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13 views

What is the “standard reference” for cascade forward neural network?

What is the "standard reference" that firstly describes or surveys in details the cascade forward neural network? This kind of net is available in matlab toolbox for long cascadeforwardnet (as early ...
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7 views

Norm-bounded input

What is norm-bounded input? The expression is used in section 4.4 of 'Building High-level Features Using Large Scale Unsupervised Learning' by Le et al. I can find papers using the term, but not any ...
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15 views

How does backpropagation learn convolution filters?

I've understood how the backpropagation algorithm uses the partial derivatives of the weights to train a normal neural network. However, I cannot quite understand how the algorithm changes the ...
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37 views

What are the advantages of ReLU over sigmoid function in deep neural network?

The state of the art of non-linearity is to use ReLU instead of sigmoid function in deep neural network, what are the advantages? I know that training a network when ReLU is used would be faster, and ...
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43 views

Optimal construction of day feature in neural networks

Working on regression problem I started to think about representation of "day of a week" feature. I wonder which approach would perform better: one feature; value 1/7 for Monday; 2/7 for Tuesday... ...
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20 views

Convolutional neural network - Using absolute of tanh on convolution output

I've watched an online lecture regarding CNN (https://www.youtube.com/watch?v=wORlSgx0hZY) that confused me a bit. At roughly 8:35 in the lecture it was stated that it is important to use the absolute ...
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22 views

Forecasting sales for multiple departments using external factors

I have got the weekly sales information for various locations for about 3 years.It has got information for 157 weeks.Also,I have got the probable external factors affecting the sales.I want to ...
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25 views

Neural network for data set with 50 samples per class?

I have classification task of 200 classes with 100 features of unknown origin and trying to make some sense out of typical three-layer neural network. I am using opencv implementation of neural ...
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17 views

Jacobian matrix in neural network

How do you calculate the Jacobian matrix using the results (weights and biases) of a neural network after training? I am working in MATLAB, if anyone has any code suggestions, that would be helpful as ...
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6 views

Are there closed-form expressions providing the VC-dimension for the multi-class case for different classifiers?

So far, I've only encountered the VC-dim for binary classifiers. I'm interested to know how this notion can be extended to the multi-class case. Are there expressions that provide bounds on the ...
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27 views

How is 'memory' implemented in Neural Networks?

I looked around into various articles on NN. I cant seems to grasp a basic idea - how a NN would remember what it has learnt? For example lets say there is a NN which was trained to recognize a ...
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15 views

Neural Networks. General approach to predict nearest future value (recognise incomplete pattern)

I need a general idea (and learn a bit of terminology as well) on how to approach the following problem: I have data coming in real-time but in uniform intervals (1s). each portion can have 1 or ...
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42 views

Neural Network Predicting Live Market Data (fun project for BTC prediction)

Made it just for fun - not for profit, wrote a neural network application that is predicting output from live data from exchange markets dealing with Bitcoin. Now just to clarify, i am not asking if ...
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22 views

The linearity of perceptrons

I am having quite a dilemma whether multi-layer perceptrons are linear in nature or not. In this wikipedia article, it is said that: If a multilayer perceptron has a linear activation function ...
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2answers
36 views

Choosing the number of principal components to retain before training a neural network for classification

I am working on neural networks and I am currently creating a perceptron that will work as a classifier for a data set of images with faces. I am required to perform pca (principal component analysis) ...
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2answers
58 views

How to verify that the ANN code is working properly?

First I'm not sure if this is the right place to post my question, but I saw some questions about ANN, and I assumed I can ask it here. I have implemented an ANN with back-propagation. I'm using it ...
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34 views

Making a neural network model more sensitive to one of its several inputs

I am currently using neural network methods in R to model energy consumption (response) based on temperatures, previous consumption values and weekend dummy variables (inputs). Unfortunately, the ...
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21 views

Multiple Neural Networks with single output neuron vs. Single ANN with multiple output neurons

Main Question Given multiple output parameters that are independent of each other, would multiple ANNs with a single output neuron give better prediction results than a single ANN with multiple ...
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19 views

Query on “Deep neural networks for object detection”

I was trying to follow the paper "Deep neural networks for object detection" at http://web.missouri.edu/~hantx/ECE8001/Presentation_papers/Deep%20Neural%20Networks%20for%20Object%20Detection.pdf. I ...
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27 views

Using PyBrain after training a network

I'm using PyBrain to create a neural network. I'm still pretty new to neural networks and their concepts. I've so far only run train() over the network, as ...
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27 views

How do I incorporate the biases in my feed-forward neural network

I'm trying to implement a FFNN. I'm doing this as an excercise to understand how biases play a role in the classification. I trained a NN using a package in R with the inputs being 1..100 and the ...