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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.

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Outputting handwritten digits with a Neural Network

I know that you can use a neural Network to recognize handwritten digits. How would you then use that same neural network to output handwritten digits in the unique style of that network? In other ...
Uriah Sanders's user avatar
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Theoretical Limitations of Achieving 100% Accuracy in Modeling Non-linear Relationships with Neural Networks

I am working on a project where I need to model a specific non-linear relationship using a neural network. The relationship is given by $y = 3x_1^2x_2^3 $. The approach involves: Preprocessing the ...
Mo McWebmo's user avatar
5 votes
1 answer
143 views

Changing output size from a model

So I am currently training some deep learning models for some basic classification problems, and I am trying to figure out if it is possible to change the output size of the model in case I want to ...
pdaranda661's user avatar
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1 answer
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How to explain missing dates to a model?

I have this dataset that I'm trying to train a neural network on. The problem is that since weekend dates are not available, I am not confident in whether the model is able to account for that. ...
Akshat Vats's user avatar
1 vote
1 answer
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Improving GPU Utilization in LLM Inference System

I´m trying to build a distributed LLM inference platform with Huggingface support. The implementation involves utilizing Python for model processing and Java for interfacing with external systems. ...
Cardstdani's user avatar
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diffusion model: can't overfit on single batch

I am training the diffusion model from diffusion policy, specifically their vision notebook, on a custom dataset. As always, I try to make a sanity check of the pipeline, by overfitting on a single ...
Felix Hegg's user avatar
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The possible integrations of physics and deep learning?

I have developed a model in my thesis which can compute the energy of a special physical system and some other useful physical quantities. Now I want to use it in deep learning somehow. Do you have ...
Wisdom's user avatar
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1 answer
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Accuracy and test_accuracy gives a result =1

I've developed a code for classifying hyperspectral images using three different convolutional neural network (CNN) architectures: 1D, 2D, and 3D. The code has two main parts: Preprocessing and data ...
user162895's user avatar
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38 views

Is it possible to train a neural network to feed into a Random Forest Classifier or any other type of classifier like XGBoost or Decision Tree?

I want to create a model architecture to predict future stock price movement as such: The Goal of this model is to predict if the price will go UP or DOWN within the next 3 months. I have tried a few ...
Evank's user avatar
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0 votes
3 answers
58 views

How do I force my NN to do nothing but memorize?

Consider a neural with N layers of size $M_n$. I want this NN to do nothing but memorize. I want it to fail if it is asked to make a classification for an input it has never seen before, I want it ...
mathlete42's user avatar
0 votes
1 answer
33 views

How good are LSTMs in generalizing when learning curves?

I'm interested in the following scenario: I want to learn a mapping that maps a function to another function, i.e. I want to approximate a functional operator. If one is unfimiliar with operators one ...
ZenDen's user avatar
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1 answer
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CS undergrad query about DS

why is learning DS so ambigious .you dont truly know what should you learn to actually do DS .web dev say has a clear path learn html css js and you can make something .i am a cs undergrad just want ...
Muhammad Umer's user avatar
1 vote
1 answer
45 views

Is there a model that can predict continuous data while also providing a level of confidence in the prediction?

The problem with Bayesian neural network seems to be that it is primarily working for classification problems. Is it possible to adjust this neural network, or even use a different model if one exists,...
tds's user avatar
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0 answers
21 views

Tensorflow optimization help - ANN unable to optimise seemingly simple time series prediction problem

A basic Tensorflow NN model is unable to optimise a simple synthetic time series prediction problem. I have tried various configurations and optimizers, but the model cannot beat a naive "flat&...
Zacciep's user avatar
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0 answers
8 views

pytorch is_leaf problem

I have a problem about is_leaf of the rotation_matrix i defined below in picture 1.Picture 2 shows how do i get rotation[i] by using getattr to get it from model_params. Picture 3 shows how do i use ...
Ecthelion's user avatar
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0 answers
27 views

Converting multiple binomial logits to multinomial

I am faced with a image classification problem with 3 classes. My existing network consists of 3 'branches' each corresponding to one of the classes. Each of these branch outputs a binomial logit ...
Farhan Ahmed Wasim's user avatar
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Recommendation: matrix factorization vs neural network training

In the case of collaborative filtering, say we have a matrix of item-item (could also be user-item) interactions. In the "matrix factorization" approach, we use algorithms such as SVD or ...
KiwiKiwi's user avatar
2 votes
1 answer
24 views

Practical Experiments on Self-Attention Mechanisms: QQ^T vs. QK^T

I'm currently exploring the self-attention mechanism used in models like Transformers, and I have a question about the necessity of using a separate key matrix (K) instead of just using the query ...
Peyman's user avatar
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Deep neural network is plateauing on a regression task

I'm training a deep neural network on temporal graph data. Currently, I'm trying to get a feel for how large / complex of a model I should aim for, so I'm trying to overfit to my smallest dataset. ...
aadithyaa's user avatar
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0 answers
9 views

Positional Encoding for FFNN?

Here is my problem: I have input [x1,..,xt,n1,..,nt,1,2,...,t] where there is a missing timestep xi, and I use neighboring time series (found with KNN) n1,...,nt to add more features, as well as time ...
Michel Hijazin's user avatar
0 votes
1 answer
40 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
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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
1 vote
1 answer
34 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
  • 111
2 votes
1 answer
159 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
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0 answers
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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
802 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
2 votes
0 answers
50 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
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0 answers
38 views

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
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0 answers
20 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
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0 answers
21 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
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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
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0 answers
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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
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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
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0 answers
12 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
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0 answers
13 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
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0 answers
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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
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1 vote
1 answer
56 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
44 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
20 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
19 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
35 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
17 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
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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
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0 answers
11 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
115 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
22 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
20 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

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