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
10 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
8 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
17 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 ...
0
votes
1answer
14 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
15 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
21 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
20 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
16 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
7 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
19 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
18 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
27 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
26 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
39 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
0answers
355 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
26 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
40 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
26 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
41 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
23 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
212 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
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 ...
0
votes
0answers
16 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
28 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
66 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
39 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
76 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
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 ...
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
75 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
49 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
52 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 ...
0
votes
1answer
551 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 ...
0
votes
0answers
34 views

Finding subgraph in multigraph using deeplearning

I have big multigraph each node represent entity with 0..n attributes(e.g. name, address, salary). My problem is: I will get for example 10 subgraph selected from user and these subgraphs represent ...
0
votes
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, ...
4
votes
2answers
374 views

Why is the manifold hypothesis true?

The manifold hypothesis is the statement that real-world high dimensional data (such as images) lie on low-dimensional manifolds embedded in the high-dimensional space. It has been tested to be true ...
0
votes
0answers
60 views

What is the practical limit to how many object classes you can detect with Faster RCNN?

I am trying to follow this tutorial where the Faster-RCNN-Inception-V2-COCO model from TensorFlow's model zoo is used to detect playing cards. I was wondering what is the practical limit to the number ...
2
votes
2answers
109 views

How broad is the meaning of “algorithm”?

This is a purely terminological question. The word algorithm, as I have learnt it refers to something like an "effective method, a sequence of steps, for doing something". There are alternative ...
2
votes
0answers
28 views

Multilayer Perceptrons for solving variational problems

Can we use a multilayer perceptron to solve variational problems? By variational problem I mean something we might encounter in the calculus of variations, for example the geodesic problem: given two ...
0
votes
0answers
10 views

About cross-entropy loss

Is there a classification scenario with cross-entropy loss such that the loss as a function of the predictor/neural net's parameters is a function s.t it satisfies the properties of (a) having a ...
1
vote
1answer
47 views

Linear Regression using a Neural Network

I am trying to create a regression model using a Neural Network. I am currently learning how to work with neural networks (deeplearning.ai) and so the model is not implemented using any existing ...