Skip to main content

Questions tagged [neural-network]

Artificial neural networks (ANN), are composed of 'neurons' - programming constructs that mimic the properties of biological neurons. A set of weighted connections between the neurons allows information to propagate through the network to solve artificial intelligence problems without the network designer having had a model of a real system.

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

How do I give weight to recent time points when predicting another closeby time point?

I am building a normal feed-forward neural network to predict the value of a masked time point using regression, e.g. I have values for x at times 1, 2, and 4, and I want to predict its value at time ...
Michel Hijazin's user avatar
1 vote
0 answers
14 views

Interpretation of this learning rate finder plot

I have the following plot relating to learning rate finder results (following the principles of Smith (2015)), made according to this code example and found the loss drop section to be very narrow, ...
Marco's user avatar
  • 111
0 votes
0 answers
8 views

Overfitting - Imbalance Classification using Deep-feed forward network

I have an unbalanced dataset, so I used SMOTEENN on the training set to resample, after training DFF,i could see the model is overfitting, could someone help me solve this? Thank You. ...
Pavithra K's user avatar
0 votes
1 answer
26 views

Unordered Set Classification Problem

In my setup I have one feature which is a sparse list representing categories. For example, let's say that we have M categories in the interval ...
dpalma's user avatar
  • 101
2 votes
1 answer
147 views

AutoDiff on different operations?

How it is possible to use automative differentiation (computational graph) on operations like - convolution? I know that 2d convolution can be represented by matrix multiplication. But what about 3d ...
Тима 's user avatar
0 votes
0 answers
9 views

Patterns in weights of trained model?

Apologies for a naive question. Let's say I am training a simple feed-forward neural network using stochastic gradient descent with a fixed architecture, learning rate, number of training epochs, and ...
user101010's user avatar
3 votes
1 answer
787 views

How does a Neural Net handle an unseen class for a Categorical Feature?

Let's say I train a Neural Net, and I have a Categorical Feature X. During training, there are only 3 classes seen in feature X; A, B, C. Now, let's say I want to make predictions from this trained ...
the man's user avatar
  • 139
1 vote
0 answers
29 views

Transfer learning for tabular data

I wonder if transfer learning can be used in tabular data similarly to how it's used in neural networks for image recognition. My idea would be to train a "general" model and then "...
Dudelstein's user avatar
0 votes
0 answers
31 views
+50

Tensorflow SegNet architecture

I was unable to find a complete description of the SegNet architecture for image segmentation (specifically, the decoder layers). Therefore, I would like to clarify the correctness of my ...
D .Stark's user avatar
0 votes
0 answers
19 views

Losing Information while resizing the image in Segmentation task using U-net

I'm using U-net architecture to build a segmentation task of image. During training I have image of size 256256 image. It works very well on the segmentation of same size 256256 or near to size 256*...
Akshit Dhillon's user avatar
0 votes
0 answers
20 views

Question about the limitations of regularization

I am training a neural network which is overfitting. Even when I increase the number of parameters, the test lost plateaus while the training loss keeps decreasing. Can regularization (like an L1 or ...
vermillion flycatcher's user avatar
0 votes
0 answers
13 views

How to build a model where each data point has different levels of information?

Let’s say I want to predict the weight of a person given information about them; height & sex. Now, let’s say that that I have additional information about roughly 50% of the individuals included ...
the man's user avatar
  • 139
0 votes
0 answers
23 views

When can we claim that the training converged?

I've been working for a while in a binary classification problem with different types of neural networks. In this particular case, I'm using an 3-layer MLP with hyperbolic tangent activation in input ...
leapofFaith's user avatar
0 votes
0 answers
8 views

Resources for writing CNN for semantic segmentation

I am intermediate/advanced in Python and new to machine learning. Most of what I know about deep learning I learned through Deep Learning with Python by François Chollet. I am trying to do image ...
utx7563yu's user avatar
0 votes
0 answers
11 views

How can I combine/pool of the results of regression with neural network?

My study has ten imputed dependent variables (plausible values). After separately analyzing each dependent variable using a regression neural network (NN), I must combine/pool the results. I tried ...
minre's user avatar
  • 1
0 votes
0 answers
10 views

graph signal in GNN

I am reading several materials about graph signal processing for a thesis on Graph Neural Network and i see that a graph signal is defined as a vector so each node signal is a scalar. In practice, a ...
endeavor's user avatar
  • 101
0 votes
0 answers
17 views

Loss increase while accuracy also increase [duplicate]

I'm training a fairly large classification model,and I'm having the below results. ...
WillWu's user avatar
  • 1
1 vote
1 answer
35 views

What's wrong with my implementation of an MLP?

I'm trying to predict housing prices from a Kaggle dataset using an MLP with 3 hidden layers (10 neurons each). Having read about MLPs and backprop in the CS229 notes, I tried to do my own ...
The_Monetarist's user avatar
1 vote
1 answer
39 views

Does using different optimizer change the loss landscape

I plot the landscape using this code, and I notice the landscape shape has changed a lot. My understanding is that the optimizer does not change the loss landscape. But now I'm confused if its just ...
user836026's user avatar
0 votes
0 answers
18 views

Do categorical embeddings leak data in time series?

I am a bit confused on this matter, I can't find any resources that touch on the following but my logic says that embeddings do introduce data leakage in time series: Considering a temporal dataset ...
idontknowmuch's user avatar
0 votes
0 answers
14 views

How to combine a classificiation dataset with a pair-wise comparison dataset

Let's say I'm trying to train a neural network that predicts a single output [0.0, 1.0] value that correlates to photo realism which I can use either in a classification setting or for ranking. I have ...
ahbutfore's user avatar
  • 191
0 votes
1 answer
18 views

How do I ensure final output shape matches input shape for a semantic segmentation task?

I trying to replicate the semantic segmentation example https://keras.io/examples/vision/oxford_pets_image_segmentation/ but train on my own data. I have 8 labels (7 features + background). My images ...
utx7563yu's user avatar
0 votes
1 answer
17 views

Pytorch backward error

Here I overcame a problem about backward, below is a simple example written by python code. the print information is that This really confused me as self.weights is used as a important part in my ...
Ecthelion's user avatar
0 votes
0 answers
34 views

deep learning for stock prediction

I am learning deep learning . Right now I am using MNIST data set, which contains tens of thousands of scanned images of handwritten digits, together with their correct classifications. My question ...
quanity's user avatar
  • 101
0 votes
0 answers
16 views

Training the neural network does not give the expected result

I'm trying to create a pytorch neural network capable of recognizing peaks in 2D graphs. Previously, I was able to get a result close to what I wanted, but it was not ideal and did not give a ...
AlterEGO's user avatar
0 votes
0 answers
6 views

Finding invariant feature areas within representation vector for each meta-class/group?

I have pairs of images which are not the same class, but are from the same meta-class/group. I have a standard CNN which produces a representation for each sample. If I have several pairs of images ...
StudentV's user avatar
0 votes
0 answers
6 views

How to derive the formula 13 in the Xavier Initialization paper

How to derive the formula 13 in the Xavier Initialization paper Understanding the difficulty of training deep feedforward neural networks from the formula 6?
mon's user avatar
  • 711
0 votes
0 answers
7 views

Temporal mismatch

I am building a predictive model to determine risk for a disease over the course of a hospital stay. I am using medical records from a hospital electronic medical record database. The predictions are ...
healthydata's user avatar
4 votes
2 answers
99 views

What is the best way to train a neural network with a variable number of inputs?

Suppose I have a neural network with 5 inputs: [A,B,C,D,E] There is only 1 output. The expected accuracy of the model should increase when all 5 inputs are ...
user18959's user avatar
0 votes
0 answers
21 views

ML Methods For Modelling Latent Variables

I have some time series predictor variables, $\{\mathbf{X}_t\} = \{\mathbf{X}_0, \ldots, \mathbf{X}_n\}$, and some other time series data $\{\mathbf{Z}_t\} = \{\mathbf{Z}_0, \ldots, \mathbf{Z}_n\}$. ...
baked goods's user avatar
1 vote
0 answers
19 views

Improving Detection Model - Adding image clarification

I trained an object detection model with 5K images, it works most of the time, but I am facing an issue, for few times, the object is not getting detected. So, I planned to retrain the model, for that ...
Vishak Raj's user avatar
2 votes
0 answers
26 views

What are the analogies between decision trees and neural trees?

How can I draw analogies between decision trees and neural trees? For example, how are splitting thresholds analogous between these models, and how can paths in a neural tree be represented in a ...
Mir's user avatar
  • 71
0 votes
0 answers
13 views

i need to improve accuracy of following code. it have 1 dataset folder having 7 folders. there are total 3076 images

importing libraries ...
raman deep's user avatar
0 votes
0 answers
9 views

Using a neural network to predict disease outcomes in individual cases

I'm working on a research project with the goal of using a neural network to predict disease outcomes for patients. I've built a neural network using Tensorflow and Keras and I've trained and tested ...
Daniel Tveten's user avatar
0 votes
0 answers
16 views

What is the advantage of positional encoding over using additional features?

Popular models such as the transformer model use positional encoding on existing feature dimensions. Why is this preferred over adding more features to the feature dimension of the tensor which can ...
kot's user avatar
  • 11
0 votes
1 answer
9 views

Connecting Flatten layer to Dense layer

I'm struggling with my neural network. In short, I need to recreate a model from anywhere on the internet, I've found a model that combines BiLSTM, LSTM and GRU. However, based on the error I got when ...
Tatiana Budanova's user avatar
0 votes
1 answer
31 views

How do transformer-based architectures generate contextual embeddings?

How do transformer-based architectures, such as Roberta, etc., generate contextual embeddings? The issue is, I haven't found any articles that explain this process.
user avatar
0 votes
0 answers
11 views

In which cases would we not like to go to the global minimum?

I would like to know in which cases we do not want to reach the global minimum. As I understand it, this can lead to overfitting. But why is this happening? And how can I avoid this in a real task?
7wafer7's user avatar
0 votes
1 answer
49 views

Fine tuning or just feature extraction or both using Roberta?

I'm reading a program that use the pre-trained Roberta model (roberta-base). The code first extracts word embeddings from each caption in the batch, using the last hidden state of the Roberta model. ...
user avatar
2 votes
1 answer
46 views

Creating a custom loss function for an image classification model where the label matters

I have the following dataset of images, where we can see the image distribution of labels below. I want to construct a loss function that, on the one hand, outputs probabilities for a specific class ...
Tom's user avatar
  • 21
0 votes
0 answers
6 views

Executing error_handler() during NN prediction

I am taking the course: Sequences, Time Series and Prediction. In this notebook we train for the first time a single layer neural network, and I see at the bottom line: executing ...
Cohensius's user avatar
  • 163
0 votes
0 answers
45 views

Weighting training instances by time in machine learning models

I am training a neural network based on data whose relevance I think diminishes based on how far each instance is in the past. I've had a look and one way to do this it seems is to 'weight' training ...
joe_credit's user avatar
0 votes
0 answers
18 views

How to handle time series data in ANN?

I want to use ANN to forecast the next #games played in my mobile game. There are 39 features: 9 features that describe the player's state (level, amount of in game-currencies, etc.) and the last 30 ...
Cohensius's user avatar
  • 163
0 votes
0 answers
13 views

Can an OCR model consistently recognize every digit of a long number correctly?

I'm working on OCR on scanned documents and we need to recognize the exact sequence of some printed numbers on it. Imagine you're reading a bank cheque serial number (16 digits) so the system needs to ...
TlifeProgram's user avatar
2 votes
1 answer
50 views

Impose minimal value on ANN prediction?

I want to predict a feature using a NN, but some business logic require that the prediction will be no less than min_value. I imposed it after the training by: <...
Cohensius's user avatar
  • 163
0 votes
0 answers
16 views

Systematic bias of neural network regression

I am not sure if here is the correct place to ask this question. I was trying to do graph-level regression task using graph convolutional networks, basically I concatenated 3 linear layers after ...
Tianjian Qin's user avatar
0 votes
0 answers
25 views

How can I use two different datasets to train an ML model?

I am trying to create a machine learning model that takes in two different pandas data-frames from a basketball stats website and given multiple variables, will output a prediction of how many points ...
jshargo7's user avatar
0 votes
1 answer
33 views

My custom neural network is converging but keras model not

in most cases it is probably the other way round but... I have implemented a basic MLP neural network structure with backpropagation. My data is just a shifted quadratic function with 100 samples. I ...
tymsoncyferki's user avatar
1 vote
1 answer
96 views

What do special tokens used for in Roberta?

When I use this code: ...
user avatar
1 vote
1 answer
28 views

Neural Network Weights - How do they know their position?

I am a copyright scholar so please forgive my ignorance. When weights are stored external to a model what is the mechanism by which the weight knows which neuron or node in a decision tree it is ...
Benjamin White's user avatar

1
2 3 4 5
88