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.

Filter by
Sorted by
Tagged with
0
votes
0answers
9 views

Weighted Average of Neural Networks with Cross Entropy Cost Function

What is the best way to create an ensemble of neural networks utilising weighted averaging when these networks were trained to minimise cross entropy error function? The literature I found (e.g. [1]) ...
2
votes
1answer
37 views

Weighted Average of Multi-Output Neural Networks

[1] discusses how to construct an ensemble of neural networks by giving each network a certain weight $\alpha_i$: \begin{equation} f_\mathrm{GEM}(\boldsymbol{x}) = \sum_{i=1}^N \alpha_i f_i(\...
2
votes
1answer
27 views

Why do we need to take the derivative of the activation function in backwards propagation?

I was reading this article here: https://towardsdatascience.com/how-does-back-propagation-in-artificial-neural-networks-work-c7cad873ea7. When he gets to the part where he calculates the loss at ...
-1
votes
0answers
23 views

ReLU vs Sigmoid

In terms of a deep neural network, which activation will perform better between ReLU and sigmoid? Does it depend on the data or is one always better than the other? Are there any associated costs?
0
votes
0answers
22 views

Are neural network latent representations fitting a Gaussian distribution?

Neural network latent (or pre-activation) representations are the weighted sums of inputs to neurons in hidden layers before applying an activation function. The vector of representations of neurons ...
0
votes
0answers
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 ...
0
votes
1answer
21 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 ...
0
votes
1answer
41 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 ...
1
vote
0answers
7 views

Is there a way to connect a deep language model output to input?

In models like GPT-2, TXL and Grover, is there a good way to know which input weights (tokens) resulted in each token of the output?
0
votes
0answers
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 ...
0
votes
0answers
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 ...
0
votes
0answers
26 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 ...
1
vote
1answer
27 views

Neural network for PDE: Should we train the PDE using more initial and boundary data at the beginning?

I was trying to solve a partial differential equation (PDE) using a neural network. The solution to the PDE is not unique unless the boundary condition is determined. In my case, the neural network ...
0
votes
0answers
20 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 ...
0
votes
1answer
24 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 ...
2
votes
1answer
21 views

Neural network game players and incremental updates

Neural networks in recent years have been successfully used for gameplaying. A difference between games and e.g. image processing is that the game boards get updated incrementally. Do any neural ...
0
votes
0answers
20 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 ...
0
votes
0answers
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 ...
0
votes
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.
0
votes
0answers
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 ...
0
votes
0answers
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 ...
1
vote
0answers
20 views

Value flow (and economics) in stacked reinforcement learning systems: agent as reinforcement environment for other agents?

There is evolving notion of stacked reinforcement learning systems, e.g. https://www.ijcai.org/proceedings/2018/0103.pdf - where one RL systems executes actions of the second RL system and it itself ...
0
votes
0answers
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....
0
votes
0answers
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 ...
0
votes
0answers
41 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 ...
1
vote
0answers
30 views

Solving analytic gradient of loss function for neural networks [closed]

Please note that I am talking in about theory rather than ''what someone would do in a real, practical situation''. Given a multi-layer Perceptron with at least 1 hidden layer, and sigmoid (or other ...
0
votes
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|| \...
0
votes
0answers
58 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 ...
1
vote
1answer
647 views

Guided Backpropagation in Deep Neural Networks

I am reading about a method called guided backpropagation. https://ramprs.github.io/2017/01/21/Grad-CAM-Making-Off-the-Shelf-Deep-Models-Transparent-through-Visual-Explanations.html#deconv-and-guided-...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
1
vote
1answer
41 views

What is the most efficient way to test whether a set $X \subset \{0, 1\}^n$ and its complement $\{0, 1\}^n \setminus X$ are linearly separable?

I am interested in algorithms that have optimal running time, and ideally which are also very easy to implement. If you can also give some tips on how to implement the algorithm(s) you mention in the ...
0
votes
0answers
31 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 ...
0
votes
0answers
71 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 ...
1
vote
1answer
24 views

List of all possible reasoning tasks - satisfiability and theorem proving only?

What is the exhaustive list of reasoning tasks? As far as I can understand, then any logical reasoning reduces to 2 tasks only: 1) satisfiability problem (finding the assignment of the variables) and ...
0
votes
1answer
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?
2
votes
3answers
215 views

Could neural networks be considered metaheuristics?

A metaheuristic is defined as a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently ...
0
votes
1answer
51 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 ...
0
votes
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 ...
2
votes
0answers
31 views

How do convolution layers work?

So let's say I have a 4x4 image with 3 channels. So we have 3 4x4 matrices where each matrix represents a channel. Now let's say I also have a 3x3 kernel. I know that for convolution layers, I have to ...
1
vote
1answer
70 views

How to train this neural network?

I seek the neural network (NN) which satisfies the 100 equations (i=1,2...100) $\sum_{j=1}^{2000} NN(A_{ij},B_{ij},C_{ij})=Q_i$. Where A,B,C are 100x2000 matrices So I know Q, A, B and C How can ...
1
vote
1answer
48 views

What does “Temporal extent” mean?

I am reading Long-term Temporal Convolutions for Action Recognition and under the Section 3.1, I read this: To investigate the impact of long-term temporal convolutions, we here study network ...
2
votes
1answer
84 views

Learning the activation function in a neural network?

Neural networks use specific activation functions, commonly used ones are tanh, ReLu. I have seen that people have experimented with continuously parametrices activation functions, for instance here. ...
0
votes
1answer
33 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 ...
0
votes
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 ...
1
vote
0answers
81 views

Converting (reverse-engineering) Turing machine into program or most concise algorithm?

It is known that every program or every algorithm can be converted to Turing machine. But what about the reverse process? Is there algorithm (or research trend that considers such algorithm) to ...
1
vote
2answers
57 views

Learning a perceptron from stream data

I want to train a Perceptron using stochastic gradient rulefrom the stream data. I have very limited amount of memory and i can store only $N$ examples. Suppose my population consist of point as ...
2
votes
1answer
22 views

Is it still transfer learning if you consider input as well as output? (neural networks)

I'm new to the CS stack exchange, so a fond hello to you all! I joined since I have a question I've been curious about. I have recently been running some experiments in transfer learning - ...
0
votes
0answers
56 views

Crossover topologically identical neural networks

I have recently learned about artificial neural networks (very interesting) and genetic algorithms (also very interesting). I have read some suggestions concerning how to crossover two parent neural ...