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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40 views

Is ANN a data structure or an algorithm?

When I read about Artificial Neural Networks (ANN), no one says what ANN is. For instance, Wikipedia says: Artificial neural networks (ANNs), usually simply called neural networks (NNs), are ...
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13 views

Using Restricted Boltzmann Machines for clustering data

I want to use RBMs as a clustering model and the idea is to use an RBM for clustering a 16 class clustering problem with 4 nodes in the hidden layer. The clustering is done by updating the hidden ...
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Edge features in Graph Neural Networks

I would like to know which GNN architectures support edge features, and where can I read about them. If anyone knows where I can search that \ how to distinguish between a GNN that doesn't inherently ...
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1answer
12 views

How many pixels support each neuron in multi-layer CNN?

I'm studying for a computer vision module and I'm on the deep learning topic, in one past paper we have the following question: Given that a convolutional neural network has five convolution layers (...
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59 views

Universal approximation bounds of the form $\|f(x)-\hat{f}(x;w)\|\leq \varepsilon \|f(x)\|$

It is known that for every $\varepsilon>0$ there is an appropriate neural network architecture, such that one can approximate any continuous function $f:[0,1]^n\to[0,1]^m$ by the neural network ...
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7 views

What are open-loop and closed-loop modes of neural networks?

I came across the following line in the book ‘Deep Learning (Ian Goodfellow) 10.2.1, pg 374; The disadvantage of strict teacher forcing arises if the network is going to be later used in an open-loop ...
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8 views

Resource Allocation problem using Deep Reinforcement Learning

I am working on a resource allocation problem, and I need some help. I have a few Jobs (J) and few Machines (M) to complete them. Each job has a size (s_j), resource demand (c_j), and a delay bound (...
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1answer
52 views

How to transform an Abstract Syntax Tree (AST) to an Abstract Binding Tree (ABT)? (for machine learning fo theorem proving)

I was reading the HOList paper that applies Graph Neural Networks (GNNs) to the HOL Light (HOList) data set and benchmark for ML for theorem proving. They describe their results etc but there is no ...
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PDE Data sets for Neural Networks?

I've only just started researching the use of neural networks to solve differential equations (I'd like some PDEs), and I have a little background in building a neural network. I was wondering if any ...
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23 views

Discrete action space for MADDPG

I am trying to apply MADDPG, a policy gradient algorithm that uses centralized training and decentralized execution, to a project. In the work of Lowe et al., the actor returns a pmf for a discrete ...
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1answer
31 views

Raising to the T in machine learning

What does it mean when in a machine learning paper there is $(arg)^{T}$, what does the T does to an arg in this 3b1b video on neural networks he puts the: $(w^{l-1})^{T}$
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88 views

Which features can be considered for neural network based SAT solving?

I'm trying to implement SAT solver, based on backtracking algorithm and BCP. This SAT solver is trying to pick one literal from each clause, from 3-CNF SAT instances. I've implemented a neural network ...
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Understanding the bias in the output layer of a multi-layer perceptron

I did a question on designing a multi-layer perceptron and I didn't understand where they get a value of -4 for the bias in the output layer from. I got exactly the same neurons for the hidden layer, ...
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Question about RNN detecting toxic spans

How can an RNN be used for detecting toxic spans (spans of words containing toxic language) in a social media comment? Specifically, what should be the input to the RNN at each time step t? How many ...
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1answer
35 views

Linear Discriminant Method as Pre-processing for inputs going into a Neural Network?

As the title suggests, I am curious whether Linear Discriminant Method can be performed on a dataset as pre-processing before putting the reduced-dimensional data as input for a neural network for ...
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1answer
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How to give the nearest text that the user has written

In my database I have 1500 food dishes, not all are single words, there are compound words like "cheese with dried fruit and nuts". And I have them in 5 languages (de, en, fr, es, it). My ...
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How to use XML format of graphs in Graph neural network?

I have manually labeled directed graphs that I need a GNN to train on and then predict when given a new directed graph, what the labels for the vertexes in the graph should be. My graphs are in the ...
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20 views

Practical ml model explainability with graphs and prolog

How practical are logic engines for proof paths combined with knowledge graphs in Providing reasonable explainability for ML models trained using GNNs? Adding more context. there is a history of ...
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1answer
124 views

How to calculate the number of weights for a CNN?

Can somebody help me with the formula needed to calculate the number of weights for a CNN, using the following sample question as the basis for it? Suppose we have a convolutional neural network with ...
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8 views

Which model to apply on such panel data with so may rows but for each unique id rows are 6-8 rows per unique id?

I am new to such panel data where I have multiple observation for same ID in different Quarter and I am not sure what kind of machine learning algorithm I can apply. I have data from Q1-18 till Q4-...
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How is the dimensionality of the volume in ConvNets determined for a general case?

In ConvNets, I understand how the dimensionality of a flat image changes after convolving it with a single filter. For example, if you convolve a P x P x 1 image (assume no padding) with a filter with ...
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16 views

Calculating error in the Input to a convolutional layer

Say I have a incredibly simple input to a convolutional layer: [In1] <----- 1*1 input matrix I have two filters applied this input:[F1] and [F2] They give the results: [F1 * In1] and [F2 * In1] ...
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Looking for references for real-world scenarios of data-poisoning attack on labels while doing supervised learning

Consider the following mathematical model of training a neural net : Suppose $f_{w} : \mathbb{R}^n \rightarrow \mathbb{R}$ is a neural net whose weights are $w$. Suppose during the training the ...
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What is spatial information in neural network? [duplicate]

The architecture of the CNN model contains several convolutional layers, non-linear activations, batch normalization, and pooling layers. The initial layers learn the low-level concepts such as edges ...
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1answer
49 views

Kohonen, 1 dimension, SOM, puzzle

We consider training one-dimensional open map of Kohonen with neurons in one-dimensional input space. We assume it is completed the phase of the device and the weights $w_i$, $i = 1,2, \dots, N$, are ...
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1answer
25 views

Is transfer learning applied on only similar datasets only?

I am trying to make a CNN model on different brands of logos . Firstly , I wrote a CNN from scratch and trained it on which I got 70% accuracy, I have total 40 classes and each class has 100 images . ...
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1answer
57 views

What is the Number of epochs with no improvement after which training will be stopped.?

I am trying to make a Convolutional neural network. Training the images of different brands of Logos. Have 100 images per class and there are 40 classes. I have trained the model now want to check ...
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1answer
42 views

How does CNN deal with rotation invariant pictures?

I am trying to make a CNN model . Training the image . Want to know that When we apply kernel on image and take out the features of images. That features are rotation invariant or we have to apply ...
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67 views

Efficient top eigenvalue computation for Hessian of neural networks

I train a neural network - one of the Resnet variations ($\approx 10^7$ parameters) on the CIFAR-10 dataset - and after each epoch, I would like to find the smallest/largest eigenvalues of its Hessian....
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How do I choose the right model for production?

I am trying to build a classification algorithm having 28 classes. These classes consists of Logo of companies like adidas , Nike etc. I have very low dataset below than 100 images and greater than 70 ...
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1answer
36 views

Which pretrained model will be best for my dataset?

I am trying to build a classification algorithm having 28 classes. These classes consists of Logo of companies like adidas , Nike etc. I have very low dataset below than 100 images and greater than 70 ...
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17 views

Testing correctness of feedforward neural network implementation

So I'm currently reading about neuroevolution (NEAT, WANN) and trying to make my own implementation just as an exercise. Now I want to test if my feedforward implementation gives the expected output. ...
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1answer
135 views

Binary cross Entropy derivative?

I am just learning backpropagation algorithm for NN and currently I am stuck with the right derivative of Binary Cross Entropy as loss function. Here it is: ...
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1answer
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If i use other peoples code for my final year project does that come under Plagiarism?

I want to make a facial recognition project but my coding ability is very limited so I am going to be relying on other peoples code but if i use their code even if i change the variables and make ...
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any hope to solve np hard problem using deep learning? [duplicate]

I know some basic machine learning and deep learning. Now a days deep learning solve many types of problem. I working working optimization problem like np, np hard problem. Is there any hope to solve ...
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New Neural Network Pruning Algorithm

I developed an algorithm that can prune down neural networks while retaining most of the accuracy. For example, it can take a trained neural network (relu activation functions) with a hidden layer of ...
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Designing a neural network with LSTM and feedforward NN combination

Currently, I'm designing a neural network that works with reinforcement learning. In summary, the agent takes in information about itself and nearby agents and, in conjunction with global world ...
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30 views

Stuck on Ejaz and Islam's paper about Masked Face Recognition

I'm relatively new in machine learning and I am trying to put together my undergraduate thesis on masked face recognition. I've read Ejaz and Islam's paper (available at https://www.researchgate.net/...
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1answer
29 views

What does non-linearity mean in Neural Networks? Why is it necessary?

ReLU units are said to be necessary in CNNs to introduce non-linearity which convolution does not involve. This is needed, because many real-world forms of data are non-linear. What does non-linearity ...
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1answer
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CNNs: Why do the filters identify more complex features as the model goes on?

I have a feeling that it's related to how different output layers are stacked up against one another? However, there's a missing link in that argument that I can't completely get to.
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Papers on depth separations in neural networks

I am new to the domain of Machine learning. I have been asked to present a paper related to the mathematics behind the depth separations in Neural Networks (by Itay Safran, Ronen Eldan and Ohad Shamir)...
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2answers
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Conv Net dimensions misunderstanding?

I was asked this question: Given an image with shape [1,28,28], what will be the shape of the output of a convolution layer with 10 5x5 kernels (filters) without padding? Now, are the shape dimensions ...
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Good Articles/book on neural networks

I search good articles or books on neural network. unfortunately i don't a lot of time and only now i started to research this field. so i actually search a good source that will give the fundamental (...
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1answer
36 views

What is the time complexity of the SGVB estimator?

In the context of variational autoencoders, we want to maximize the evidence lower bound and this is typically done using Stochastic Gradient Variational Bayes (SGVB). I was curious if there is any ...
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1answer
20 views

which neural network is good for predecting the value of strings

I have a dataset that contains some strings. A numeric value is assigned to each string. I want to develop a machine learning (deep learning) model to get a string and predict its value. What neural ...
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1answer
49 views

How to estimate the computational cost in a neural network?

Given a neural network(assuming no regularisation/dropout), I want to determine the computational cost of doing a forward and a backward pass of a datapoint. I want the measure to be of independent of ...
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1answer
41 views

Expressivity of neural networks, how much information can be stored

I want to know whether a given neural network (with a finite number of nodes) is able to store all injective maps f: D -> C, where D has cardinality k and C has cardinality N (so the number of maps ...
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Why can't we say that a Neural Network is a NP problem solver?

From this video lecture from MIT https://youtu.be/moPtwq_cVH8?t=1229 there is mention how NP complexity works with finding a "lucky" algorithm and luck can never be accounted for. The ...
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23 views

Signal translation with Seq2Seq model

I'm currently doing some research on signal processing and I got a dataset which includes the signal in itself and its "translation". So I want to use a Many-to-Many RNN to translate the ...
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1answer
34 views

object image segmentation

I have 2 different datasets with similar objects, one where each object is 50 pixels wide and the other where they are 150 pixels. Each photo is 512x512 for both datasets. These two datasets have the ...

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