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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understand stride in cnn

I am studying the network in this article. The network accepts input dimension $129\times8$. The CNN layers have kernel size [9 1] and stride [1 100]. I don't understand how [9 1] kernel works with ...
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Sensitivity analysis of Multi-Layer Perceptron Neural Networks

I have used Multi-Layer Perceptron Neural Networks to do a binary classification. Now I want to perform a sensitivity analysis. I am planning to follow a similar approach to this paper. In case the ...
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What is the computational class of neural networks?

What is the relationship between formal automata and the different types of neural networks? What kind of neural network would you need to be able to parse context-sensitive languages, for instance?
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How many model parameters do R-CNN, Fast R-CNN and Faster R-CNN have respectively?

I am making research on object detection and I would like to know the number of parameters these three models obtain.
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Can I think of neural networks as special cases of computational graphs?

I'm a rookie. I'm recently learning computational graphs and automatic differentiation. I think neural networks are special computational graphs, is this right?
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Which artificial neural network model can I use when trying to predict gene expression?

I need help choosing a model for my data. I have 50 genes that I need to test for their expression levels. I was thinking of implementing either an SVM or a multilayer perceptron model where the ...
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Is DQN with experience Delay a thing?

I am currently working on my master thesis on Mobile Edge Computing where I follow a paper called "Deep Reinforcement Learning based Computation Offloading and Resource Allocation for MEC" ...
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Is Self-Modifying Turing Machine equivalent to NTM or TM?

Let SMTM be Turing Machine, but the commands recorded in which can change to others in some random way (for example, choose with a 50/50 probability the command to move to the right or move to the ...
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Reusability of neural network architecture

I have created a neural network architecture by considering the data for one study area. Have performed grid-search to tune almost all the hyperparameters of the model. I want to use the same network ...
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Function or signal as machine learning model output

I am new to machine learning. I've seen that machine learning models such as ANN or SVM can be used to predict scalar outputs taking scalar inputs. I am trying to create a model that has a number of ...
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Spherical Convolutional Neural Nets

I have been reading this paper https://arxiv.org/pdf/1801.10130.pdf discussing Spherical Convolutional Neural Nets I am trying to wrap my head around what a SCNN is and what it accomplishes over ...
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What type of neural network or machine learning technique would you apply?

I want to predict as many time steps of a variable (X) as possible. The more time steps forecasted, the more successful the solution proposed. To the best of my knowledge, applying LSTM neural ...
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Hand Landmark Coordinate Neural Network Not Converging

I'm currently trying to train a custom model with tensorflow to detect 17 landmarks/keypoints on each of 2 hands shown in an image (fingertips, first knuckles, bottom knuckles, wrist, and palm), for ...
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RMSProp Momentum and Decay

I'm making an application of MobileNetV2 and according to their article: We train our models using TensorFlow. We use the standard RMSPropOptimizer with both decay and momentum set to 0.9. We use ...
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How to train (and perform the test phase using only matrix operations) in the article "Infinite attention: NNGP and NTK for deep attention networks"?

My question is about the article Infinite attention: NNGP and NTK for deep attention networks. I am looking for a closed for solution (using only matrix-matrix/matrix-vector/inverse matrix-vector ...
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Function that returns weight gradient for a fully connected neural network

I am trying to implement the Backpropagation algorithm in C for a specific network, but I'm struggling with the derivatives involved and how they relate to the actual gradient in each weight. My final ...
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How can I modify this detail in the article ""Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications"?"

This question is about de paper Extending Multi-Sense Word Embedding to Phrases and Sentences for Unsupervised Semantic Applications. I am interested in the transformer part of the paper and the main ...
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Are the weights between hidden layers changed in backwards propogation? If so, how

If so, I don't understand how, as a large part of back propogation is knowing what the 'real' answer is in comparison to 'predicted' answer. With 1 neuron in a hidden layer, we do not know the 'real ...
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Predicting continuous variables using image and text input to neural network

I am looking to create a neural network architecture that takes two kinds of inputs: image and text, and outputs a predicted continuous variable. More specifically, I will have multiple images of a ...
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How should I create a word-embedding an NLP model recognizing HTML elements?

I'm currently doing a small-time project where I have to deliver a model which can classify specific elements on a web-page using the HTML code. For this, I have considered using the HTML tags for ...
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Is there any other type of network architecture difference?

I'm learning a bit about network architecture.I wonder if there are other types of network architecture besides a perceptron and simple multi-layer networks? And two popular applications of its
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Mutual Friends in a Network?

I always seem to have trouble finding a formal way to analyze this (be through proofs or whatever). The problem statement is as such: If A and B are friends, and B and C are friends, then A and C are ...
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What is the primary reference for the observation/discussion of how neural networks struggle with ambiguous training datasets?

It is known that neural networks, such as convolutional neural networks, struggle with pattern recognition if training sets contain ambiguities (i.e. several labels can correspond to one and the same ...
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How long can the short memory last in the RNN?

For a recurrent neural network, the LSTM was a model of how the network worked. However, consider the case where an input was a long paragraph or even an article. $$c_1c_2...c_n$$ where $c_i$ were ...
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Convergence of graph neural networks (GNNs)

I'm new to the area, and we don't have a course on graph neural networks at our university. However, I will still like to know the main theoretical results when considering convergence of graph neural ...
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Can we supervise on the hidden states of RNN?

I'm trying to generate some history-dependent model with machine learning, whose underline physical model has a clear definition of its "internal state variable" (a state derived from ...
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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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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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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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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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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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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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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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2 votes
1 answer
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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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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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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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1 answer
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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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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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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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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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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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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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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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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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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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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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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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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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