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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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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 ...
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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,...
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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&...
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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 ...
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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 ...
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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 ...
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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 ...
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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. ...
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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 ...
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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 ...