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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14 views

Input/Output encoding using LSTM for time series prediction

I have a rather basic problem with understanding LSTM neural networks. My basic problem is predicting a timeseries of data points. Each time step gives me a single value which can be encoded as a ...
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5 views

When a trained RNN is tested, is the number of time-steps same for every input?

I am a beginner in deep learning so bear with me. If I want to unfold the RNN in order to represent the relation of output to the input as a non-recursive functions I would have to know the number of ...
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42 views

DFAs can be encoded as input/output for a neural network?

I would encode DFAs (Deterministic Finite State automata) as output (or input) of a neural network for a supervised learning; it is well-known [1] that efficacy of neural network training strongly ...
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1answer
30 views

Understanding the Broyden–Fletcher–Goldfarb–Shanno Algorithm to Select Weights for Neural Nets

I am trying to train and implement a Neural Network. I was reading a few articles, learning about their principles and the math that goes behind them. However, while I was trying to understand the ...
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33 views

Neural Network Design For Robotic Car

I have a working ANN structure capable of learning and i want this network to be able to drive a line following robot for competitions. I have 8 sensors 2 motors(left and right) and system is going to ...
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17 views

Noise injection to fight overfitting

I've been researching the specifics of adding noise injection to my input layer to fight against overfitting. It seems as though most papers recommend a Gaussian noise vector with mean of 0 and a "...
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1answer
98 views

Are basic CS algorithms used in machine learning?

I have read some articles which state that basic algorithms such as dynamic programming , graph algorithms etc are not required int machine learning fields such as deep learning , reinforcement ...
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28 views

How to best model multidimensional, continuous, non-convex “shape” as neural network?

I have: set of n-dimensional points that I know are inside of the shape n >= 18, range on all dimensions has upper bound and lower bound (no axis goes to infinity). shape is pretty large in this n-...
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21 views

Understanding This Graphical Depiction of a Radial-Basis Function Network

Let $f$ be a Radial-Basis Function Network: $$ f(X) = \sum_{i=1}^N a_i p( \lvert \lvert b_i X - c_i \lvert \lvert) $$ From Artificial Intelligence for Human Beings, the following depicts $f$: In ...
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1answer
31 views

Batch regularization & L2 regularization

Is performing batch regularization in addition to L2 regularization redundant? Batch regularization: http://arxiv.org/abs/1502.03167 Notice how when performing batch regularization, you forgo using ...
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1answer
62 views

Which neural network topology is the most efficient to generate randomly shaped letters?

I have created some unique shapes, so-called "letters" for a custom alphabet, all of which can fit into 9x9 pixels. Instead of drawing countless more, I try to combine two solutions I saw in a ...
2
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1answer
29 views

HOG vs. neural networks for person detection

I am very new to computer vision, (a high school student) and I am working on a project to count the number of people present in a room. I have tried to use the HOGDecriptor for person detection ...
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1answer
13 views

Best practices for normalizing up training, validation, and test sets

I was reading up on how to normalize my training, validation, and test sets for a neural network, when I read this snippet: An important point to make about the preprocessing is that any ...
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1answer
31 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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1answer
16 views

Optimizing neural network on small training set

I'm in the process of optimizing my neural network. I'd like to optimize on a small training set (1000 rows) as opposed to my full training set (100K rows) for speed reasons. Will the optimal hyper-...
4
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1answer
44 views

Classification training data, but regression prediction

Suppose I'm performing machine learning on a simple dataset, and have a bunch of training data of the form: ...
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1answer
16 views

Time Series Prediction with an LSTM

I have a time series that I want to predict with an LSTM. I am able to get very good results using 50 datapoints predicting 51, but I struggle to get any accuracy using something like 200 datapoints ...
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1answer
17 views

error computation in multi layered perceptron

I was reading about Multi Layered Perceptron(MLP) and how can we learn pattern using it. Algorithm was stated as Initiate all weight to small values. Compute activation of each neuron using sigmoid ...
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1answer
43 views

Is it okay to select the best performers of test cases for scientific publication in neural network machine learning [closed]

If I split my data properly into 75% train, 15% test, and 15% validation, and there are over 100,000 samples, is it appropriate for me to train 100s of neural networks then select only a couple based ...
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1answer
20 views

How to determine the dimensions of the bias vector in a Convolutional layer (tensor flow)?

In the Deep MNIST Tutorial for Tensor Flow, (and in general), we create a convolutional layer with a weight tensor, say W, of shape (patch dims, #input channels, # output channels). However, when we ...
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1answer
37 views

In what way can Google deepdream be extended? Is there an image dataset which can use hallucinations produced? Any deepdream useful application? [closed]

Is there a particular section of image data which when trained on deepdream algorithm and given some input image produce a resulting image from which we can conclude that the deepdream can be used for ...
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0answers
14 views

choosing right neural network architecture and input features

As a short example suppose we have a kind of pollution sensor and a jet fan in a tunnel. Jet fan turn on/off according to the automatic scenario based on pollution sensor value. Pollution itself ...
4
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1answer
81 views

Is there a “flaw” in the backpropagation algorithm?

While trying to find a better backpropagation algorithm, I came across a paradox in my algorithm and then I found out this also happens in the usual backpropagation algorithm. Our neural network ...
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0answers
25 views

Detecting facial texture [closed]

I would like to extract facial texture information to feed into my neural network as an indicator of beauty. The data set I am working with is just a face framed by a black box. Each face is centered ...
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0answers
17 views

How to calculate total variability matrix?

I'm writing paper about speaker recognition using artificial neural networks and currently I'm stuck with one thing. There is a Gaussian Mixture Model (GMM) that we can use to represent speech and it ...
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0answers
66 views

How to design deep convolutional neural networks?

As I understand it, all CNNs are quite similar. They all have a convolutional layers followed by pooling and relu layers. Some have specialised layers like FlowNet and Segnet. My doubt is how should ...
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31 views

How do neural networks create results like its inputs?

I understand basic neural networks (input layer, hidden layers, output layer) and gradient descent learning. However I keep hearing about news talking about neural networks painting and making jazz ...
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1answer
25 views

Difference between SNN RL and DNN RL?

In Reinfrocement Learning (RL) in Neural Networks (NNs), I've seen two approaches to Q-learning. The first is to tile the state space with basis functions using Spiking Neural Networks (SNN) to ...
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27 views

Are the Confabulation Theories of Thaler and Hecht-Nielsen Isomorphic?

Both S. L. Thaler and R. Hecht-Nielsen have set forth neural-based theories of "confabulation" applicable to machine learning. The essential mathematics of Hecht-Nielsen is set forth in his paper "...
2
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1answer
38 views

Predicting next action to take to reach a final state

Does anyone know of an algorithm that could be used to determine the next action to take to reach a desired state when trained on time-series data? For example, a robot starts at a certain state, ...
2
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1answer
95 views

Why Isn't This Outlier Score/Reconstruction Error Not Squared?

I was looking through a paper called "AI2 : Training a big data machine to defend", and saw this (http://people.csail.mit.edu/kalyan/AI2_Paper.pdf) $score(X_{i}) = \sum_{j=1}^{p} (|X_{i} − R^{j}_{i}|)...
2
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1answer
24 views

Determining if my artificial neural network needs additional layers

I have implemented a neural network for load forecasting in Microsoft Excel. My structure is very simplistic and involves only 1 hidden layer and 4 neurons. (See picture) I trained my network with ...
1
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1answer
14 views

Using a combination of spatial and non-spatial inputs for convolutional neural networks

I'm working on training a game AI using deep reinforcement learning to achieve specific examples based on pixel input and some additional state information. Naturally, I'm using a convolutional ...
2
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0answers
15 views

How does a recurrent connection in a neural network work?

I am reading a very interesting paper on genetic algorithms which define neural networks. I am familiar with how a feedforward neural network operates, but then I came across this: Where node #4 ...
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27 views

KDD Machine Learning using K-NN Algorithm Classification Problem

I'm trying to solve a classification problem from the KDD cup archive of 2004. Details can be found here: KDD 2004 Archive I'm only dong the particle physics part. The description of dataset is as ...
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0answers
13 views

understanding kohonen self organized feature maps

I was learning self organizing feature maps the other day. I want to intuitively understand it because I'm not that good at math. But I still am not very clear about it. I can easily understand ...
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0answers
20 views

recommendation of topics before learning neural networks?

I have seen that the material for learning neural networks is huge, some books take a more practical approach, while others rely more on math and others in statistics. My question is direct what ...
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2answers
82 views

How do you determine the inputs to a neural network?

I'm looking at this tutorial on neural networks. The data that is given from the UCI study includes various attributes, such as "mean x of on pixels", "total # on pixels" etc, which are taken as input ...
3
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1answer
35 views

How do RNN's handle providing output with different dimension than input

It seems like an RNN has to have the ht-1 needs to be the same size as the input vector since they're being added to one another, but if you're doing something like modeling to another language or ...
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1answer
20 views

Some basic help about Artificial Neural Networks

Firstly, I'd like to state that I do not have any basis to the Neural Networks and I'd like you to recommend me very simple and understandable resources. While I have a home assignment for a short ...
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0answers
11 views

How does the k-NN method influence the relevance of the query that are more similar?

Question: 'One could argue that images that are more similar to the query are also more relevant. Discuss and explain two ways to have the k-NN method take this into account.' I thought it was ...
2
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1answer
46 views

Role of computational power in recent AI developments

Today Google's AI won its first game of Go against Lee Sedol, one of the best Go players on the planet. Image interpretation and self-driving cars are other recent success stories in machine learning. ...
3
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2answers
247 views

Traveling Salesman Problem with Neural Network

I was curious if there were any new developments in solving the traveling salesman problem using something like a Hopfield recurrent neural network. I feel like I saw something about recent research ...
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0answers
21 views

RNN giving unique outputs every time and generate a finite vector

How come RNN give unique outputs each time? If the goal of an RNN is to give the most probably completion of an input statement, why do they typically provide unique outputs if you run them for ...
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1answer
114 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?
3
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1answer
96 views

Convolutional Neural Network Feature Engineering?

I'm working through the tensorflow tutorial, and I see how you go from 28 x 28 to zero-padding and applying a 5x5x32 convolution to get 28x28x32 and max-pooling etc. What I'm confused about is the 32 ...
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0answers
63 views

Difference between Elman, Hopfield & Hemming Recurrent Neural Networks

What is the main difference between Elman, Hopfield & Hemming Recurrent Neural Networks? Python Neurolab Library examples: Elman Recurrent Neural Network Hopfield Recurrent Neural Network ...
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43 views

What are the limitations of LSTMs?

For a school project, I'm planning to compare Spiking Neural Networks (SNNs) and Long Short Term Memory (LSTMs) networks in learning a time-series. I would like to show some case where SNNs surpass ...
3
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3answers
204 views

what is difference between multilayer perceptron and multilayer neural network?

When do we say that a artificial neural network is a multilayer Perceptron? And when do we say that a artificial neural network is a multilayer? Is the term perceptron related to learning rule to ...
4
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56 views

Flaw with Cross Entropy Error in Neural Networks

I've recently been working on creating a neural network to classify handwritten digits. I implemented 1-of-N encoding such that there are the same number of output nodes as possible digits (The ...