# 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.

409 questions
Filter by
Sorted by
Tagged with
18 views

### Using a point as an input for a perceptron

Can a point be used as the input for a perceptron/neural net? The relationship between the two numbers that make up a 2D point does not affect the output, but does this not matter when the ...
16 views

### Information bottleneck function

On the Wikipedia page, the information bottleneck function is defined in the following way. For some random variable $X$ containing information about the relevant variable $Y$, we have some joint ...
15 views

### Training a model from self-play

Say I'd like to build a Go AI. The Go AI takes in the board state and then predicts who's more likely to win from that state. When I want to make a move, I just test every next board state I could ...
15 views

### How does the forget layer of an LSTM work?

Can someone explain the mathematical intuition behind the forget layer of an LSTM? So as far as I understand it, the cell state is essentially long term memory embedding (correct me if I'm wrong), ...
32 views

### Text detection in computer vision

I'm curious about the way text recognition works in machine learning(or more generally, the question of object vs not object) in computer vision. How are systems trained when the not-object data set ...
25 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. ) ...
45 views

### Weighted Average of Multi-Output Neural Networks

 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(\...
27 views

24 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 ...
30 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 ...
23 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 ...
47 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 ...
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?
14 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 ...
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 ...
30 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 ...
32 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 ...
40 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 ...
25 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 ...
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 ...
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 ...
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 ...
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.
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 ...
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 ...
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 ...
8 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....
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 ...
50 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 ...
32 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 ...
18 views

54 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 ...
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. ...