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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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60 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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1answer
30 views

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
21 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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43 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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582 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
76 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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30 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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1answer
58 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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1answer
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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69 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
443 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
105 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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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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275 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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1answer
70 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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1answer
64 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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823 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
586 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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1answer
317 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
496 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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2answers
456 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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1answer
412 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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1answer
184 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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29 views

Cross entropy minimization

I just read about Cross-entropy cost function for my work. and I see a notation that said "the minimization of cross entropy of some data is equal to maximization of their log likelihood." How can I ...
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1answer
17 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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1answer
38 views

Explain this Neural Network used for Knowledge Graph Embedding

I am interested in better understanding the neural network used in the paper LogicENN A Neural Based Knowledge Graphs Embedding Model with Logical Rules. To my knowledge this is the most advanced and ...
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13 views

Risk score from Neural Network classifier (more than 2 categories)

I am trying to use a Neural Network to perform multiclass classification. The classes represent Insurance Risk Level. The most risky level is Level 1, the least risk corresponds to Level 10. The ...
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9 views

Deep Learning Technique for Image to Video Conversion

I'm trying to build an engine for the following task: I have n videos, from which I've taken 1 snapshot each. I am trying to train a classification algorithm on these n snapshots. Till now I have ...
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23 views

Graph neural network

I'm trying to build GNN model that classify images , the first step is to model each image with graph , each node represents one pixel , now how can I define the edges in my case ? does the spatial ...
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0answers
19 views

On the growth rate of Leela Zero compared to AlphaGoZero

There are not many sources online, but one reference from January says of Leela Zero (LZ) that: The strength depends on the hardware and on thinking time, but from the thread "LeelaZero ...
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1answer
23 views

Which algorithm for predicting the next word(s) based on previous words, given a sentence?

I want to input some words, and out comes the next word(s). Neural nets are really hot at the moment, and I'm afraid of throwing a neural net at something, when one is not really needed. Or... maybe ...
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19 views

How to transform an arbitrary graph into a fixed vector representation?

Actuality I work in computer vision, specifically on a problem known as "scene graph modeling." This problem aims to convert an image $I$ in a graph $G=(V,E)$ where the nodes $V$ represent the objects ...
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8 views

train mlp (input x, outpu y) with a blackbox labeling any x-y pair good/bad

Standard MLP (map x to y) is trained with a set of x-y data points. My question: What if there is no train data, the only supervisor lables any x-y pair with 0 or 1. The goal is that the x-y pair ...
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28 views

CNN Predicting One Class and Accuracy Getting Stuck

My model is a binary classifier. With the same exact architecture, the model sometimes gets high accuracy (90% etc), other times it predicts only one class (so accuracy is stuck at one number the ...
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11 views

Model suggestion for detection of malware based on multiple api call sequences

I'm trying to build a RNN (LSTM) model for classification of binary as benign/malware. The data structure I've presently looks as follows ...
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7 views

Query about an equation in GAN-NMT paper

So i was studing the paper Adversarial Neural Machine Translation by Lijun Wu1, Yingce Xia2, Li Zhao3, Fei Tian3, Tao Qin3, Jianhuang Lai1,4 and Tie-Yan Liu. The link to the paper is : https://arxiv....
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7 views

Constructing a 3D virtual map from 2D Depth Maps and $x, y, z, \theta$ coordinates

Context: I intend on building a quadrotor which can generate a 3D virtual map of an area of which it captures photos with a 2D camera. I plan on first training a Convulational NN to return a depth ...
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32 views

Training of two 3x3 convolution layers vs training one 5x5 convolution layer

I'm not 100% sure this is the right stackexchange, please feel free to redirect me to another one. I know that two 3x3 convolution layers can be equivalent to one 5x5 convolution layer. I also know ...
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0answers
17 views

Computing analytic gradients for NN cost function with 1 hidden layer

Given a simple artificial neural network with 1 hidden layer, I want to compute the analytic gradient, to gain a better understanding. Using a simple loss function L such as: $L=(1/N)\sum_{k=1}^N|| \...
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44 views

Can someone explain the difference between MCP neurons and Perceptrons?

I am getting confused with the literature. Is a perceptron simply a network of MCP neurons? From what I understand, in 1957 Rosenblatt developed the perceptron based on relaxed constraints from the ...
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25 views

Interpretation of statement over probability distributions and functions

Consider the following paragraph from this research paper: To learn the generator’s distribution $p_g$ over data $x$, we define a prior on input noise variables $p_z(z)$, then represent a mapping ...
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27 views

Question about implementing GAN(Generative Adversial network)?

so most examples I've seen create two NN-s, train them, then they stack them, make the discriminator part untrainable and then train this stacked NN, why do we do this? So loss, that is calculated on ...
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10 views

Higher order reinforcement learning - that discovers new states and new actions (possibly in lifelong learning setting)?

Is there higher order reinforcement learning, that can not only find rewards (and hence optimal policy), bet that can also find the necessity to introduce new states and actions to better model the ...
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29 views

How to compute the derivative using chain rule of hidden layer (more than 5 neurons for hidden layer) with bias

In the given problem having 8 inputs with 5 hidden layers and 3 output layers and bias(b1) on hidden layers and bias(b2) on ...
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55 views

Sorting using AI / neural net

I have a search operation taking place on a server that essentially queries images using OpenCV against other images from a database. Since each image query ...
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1answer
50 views

How can we get small test error reducing only train error?

My question is about mathematical part of machine learning algorithms, especially about using it in neural networks. We train network reducing train error and I was thinking about how then test error ...
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0answers
18 views

Loss function for spurious data

I am trying to train an autoencoding neural network (autoencoder) to reconstruct seismograms. Previous studies employing this technique (e.g. Valentine & Trampert, 2012) used an L2 (mean squared ...
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1answer
31 views

How do we fix area of detectors in object detection?

I have gone through various articles on medium and also some from other sites trying to understand SSD. I am able to figure out most of the things from articles except this one. They always say that ...
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0answers
10 views

Architecture of ANN to combine images

I am interested in ANNs that combines two images in single as it shown below: But I don't know how it's called and can't find any papers or tutorials about the architecture of these ANNs. Can ...