Questions tagged [neural-networks]

For questions about a artificial networks, such as MLPs, CNNs, RNNs, LSTM, and GRU networks, their variants or any other AI system components that qualify as a neural networks in that they are, in part, inspired by biological neural networks.

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Impact of imbalanced dataset on CNN model performance

I trained a 1D CNN model to model bacterial plate count based on time series data of water temperature. Bacterial place count is numerical, based on which I created two category variables, namely &...
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What can I infer if my model is converging extremely fast?

I am running a model with fixed hyperparameters. To my surprise/shock, the model converged extremely fast with the least loss possible. I want to know the causes of this phenomenon. I have the ...
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Universal function approximation theorem on 10000 different functions

I have a NN which is trying to learn 10,000 different delay functions based on the coordinates of the matrix it exists in (a 100x100 matrix, each cell containing a different function.) By a different ...
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1answer
43 views

Why doesn't the high precision of neural network weights improve accuracy?

Consider the following paragraph from the subsubsection 3.5.2: A dtype for every occasion chapter named It starts with a tensor from the textbook titled Deep Learning with PyTorch by Eli Stevens et al....
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1answer
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Reason why chess neural network might not be training

I've been trying to use a Stockfish-like chess evaluation neural network for the past few weeks but to no avail. I wanted to get some other opinions about why my current methods haven't worked. Input: ...
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1answer
38 views

Is there any advantage in viewing weights of a neural network as random variables?

In artificial intelligence, especially in machine learning, the inputs and outputs of neurons in a neural network can be viewed as random variables. And this view is highly useful in many ways. The ...
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Find companies that offer AI solutions [migrated]

I have a social media app and currently, the feed is simply chronological. I need a personalized recommendation feed like Facebook, Instagram, TikTok, Netflix, Reddit etc. I mean there are many many ...
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How to predict the possible next moves of cars from given first moves?

I want to find the next moves of cars from the previous moves, but I could not figure out what should I use as an algorithm. Can you help me to find a way to solve this problem? I have a lot of car ...
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What is the motivation behind NAS Bench 201 research?

I recently read the "paper NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search", which can be found here. I can say that I understood most of the paper but I am not ...
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1answer
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How to output an integer/discrete number n with a single output neuron?

Say I have a game with 4 base actions [left, right, up, down] and then a value n, which determines how many times the chosen action is repeated. For example, action = left, n = 3 -> go left 3 times....
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Transforming Moving Position Data to an Inputvector for Neural Networks

Imagine a car is driving on a long street (= x-axis). The car can go in both directions and it will arbitrarily change its direction. I'm trying to formulate an Inputvector to tell a neural Network ...
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How can I adapt a trained neural network model to learn from newer data containing additional features?

We shall assume that we have a trained neural network model for some task $A$. The dataset used to train the model contained some $n$ features per sample. Using this dataset, we were able to train a ...
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Deep learning and machine learning [duplicate]

If I was Given a set of large training examples (xi,yi), how can training a neural network (NN) via stochastic gradient descent differs from using regular gradient descent in terms of the mathematical ...
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How to represent billions of time series data? [closed]

I have a time series (e.g., temperature data) from 1st January 2003 to 31st December 2017 with a one-second sampling rate, which indicates there are about $24 \times 3,600 \times 365 \times15= 473,040,...
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1answer
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What is meant by "lateral connection" in the context of neural networks?

A class of CNN is popular due to the implementation of residual connections. We can use both terms "residual connections" and "skip connections" interchangeably as they refer to ...
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35 views

Is there an approach where the output of one neural network is used to choose the next neural network?

I'd like to design a deep learning architecture in which the output of a primary neural network $M_{\theta}$ determines which neural network $N^i_{\alpha}$ in a set of secondary networks $\mathcal{N}$ ...
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1answer
56 views

Why is the performance of my neural network to predict if the mean of a randomly generated tensor is greater than $0.5$ not good?

I want to train a NN for classification the input x is a random tensor the output y=1 if ...
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1answer
55 views

What is the benefit of using a neural network instead of a look-up table in this case?

Assuming one has collected the 24 pairs of the input-output datasets for a target system: One can create a simple lookup table to describe the input-output behavior and utilize this as a controller. ...
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2answers
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How to make NN distinguish between two types of functions (data)?

I have a neural network which is trying to predict two types of functions in a noisy setting. The input is an array, and the output is also an array. The two types of functions I am trying to predict ...
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1answer
32 views

Are there any algorithms (even backtracking variations) that solve the sudoku in a way more similar to this approach?

I looked a bit online for Sudoku solvers and it seems like all the answers I found involve a backtracking algorithm. However, this is not how humans (at least not me) solve Sudoku. We don't place in ...
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Is my calculation of the partial derivative of the cost function with respect to a single weight in the first layer correct?

I'm trying to understand the chain rule of backpropagation. This is what I understood. Is it correct? $$ \frac{\partial E }{ \partial w} = \sum_{i} \frac{\partial E }{ \partial a_i^{(l)} } (\sum_{j} \...
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GAN performance starts to get worse as training continues

I'm currently trying to train a GAN to recreate similar images from a dataset. The dataset is using the Eiffel Tower Pictures from Googles Quick Draw dataset. The images aren't very large (only 12x12 ...
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29 views

Could anyone please explain this sentence about training in parallel?

One way to reduce the computational complexity of hidden state recurrences is to connect a unit's hidden state to the prior unit's output rather than its hidden state. The resulting RNN has a lower ...
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1answer
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What inherent quality of a function makes it treated as either loss or evaluation metric?

A neural network model needs a loss function for training. The neural network needs to minimize the loss function. A neural network is evaluated after training using a metric. The neural network needs ...
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1answer
69 views

Why is the cross-entropy a cost function?

The question looks foolish, but I think cross-entropy is somewhat weird as a cost function. As a cost function for linear regression, the mean square error $ \sum_{i=1}^{n} (y_i - (ax_i+b)) ^2$ seems ...
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why GPU is faster than a CPU to train or made inference in a neural network? [duplicate]

Is because a GPU is composed by a lot of proccessors (more than 1000)? what kind of calculous is better to perform on a gpu instead of a CPU?
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1answer
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Custom Tensorflow loss function that disincentivizes all black pixels

I'm training a Tensorflow model that receives an image and segments the image into foreground and background. That is, if the input image is w x h x 3, then the ...
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1answer
39 views

Does higher FLOPS mean higher throughput?

I understand that FLOPS means floating-point operations per second, and throughput is the number of inputs (for example, images) per second. If a model has higher FLOPS, it means it performs faster. ...
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25 views

Why is there a Hessian diagonal approximation? And when can we use it?

This topic has been introduced in "Pattern Recognition and Machine Learning, Bishop, 2006", section 5.4.1. I am a bit confused about this method and I have two questions. Why this method ...
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16 views

Model for predicting whether an event will or will not happen

I am not very learned in the realm of ai and coding, but want to try to learn! There's a specific type of model I'm looking for but don't know how to find. I want to see if ai can predict the chances ...
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41 views

Can I help my neural network if I know the sign of the relationships between inputs and outputs

I am attempting to train a neural network where I can say the following: For most inputs, I know the sign of the relationship between that input and several specific outputs. I.e. whatever set of ...
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1answer
46 views

How to uniquely associate a directed graph with a feedforward neural network?

I want to write an algorithm that returns a unique directed graph (an adjacency matrix) that represents the structure of a given feedforward neural network (FNN). My idea is to deconstruct the FNN ...
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1answer
42 views

For the VAE, should the input, output and latent variable code be random variables?

For a variational autoencoder, we have input $x$ (assume 1 data point for now, like an image), a latent code sampled from the decoder, $z$, and an output $\hat{x}$. If I were to draw a diagram for the ...
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Which one is better for interpolation and extrapolation: the multi-layer perceptron or radial basis function network?

Artificial neural networks (ANNs) can be used for function estimation, furthermore, in order to estimate a function there are two techniques: interpolation and extrapolation Between the multilayer ...
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18 views

Approach for model architecture and output type for decision making problem

I have a problem where based on some textual rules and images, the model has to decide what action to take. Like, if there are 5 possible selections then which object to select based on image and ...
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Is there some way for us to know if the neural network internally finds an association between labels?

I have a question about the association between labels. Say my neural network performs multi-labeling in its output layer. Now, if one of the labels is for whether a person lives in city $X$, another ...
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How can a neural network distinguish a rotated 6 and 9 digits?

Rotated MNIST is a popular dataset for benchmarking models equivariant to rotations on $\mathbb{R}^2$, described by $SO(2)$ group or its discrete subgroups like $\mathbb{Z}^{n}$: Group equivariant ...
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19 views

Fine tuning BERT for token level classification

I want to try self-supervised and semi-supervised learning for my task, which relates to token-wise classification for the 2 sequences of sentences (source and translated text). The labels would be ...
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1answer
26 views

What kind of NN to use to find misprints in test

I have a bunch of unique full names of users. I made pseudo-physical model to emulate misprints of desktop and mobile users (hence, fatfingering, jumpy fingers, accidentals touches of touch bar etc.) ...
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57 views

How can Siamese Neural Networks accept a variable number of inputs?

Traditionally, Siamese Neural Networks have two inputs. With some tweaking, you can get them to accept any number of inputs. What I don't understand is how to get them to accept variable numbers of ...
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69 views

Which ML algorithm is the best for predict the next PRNG generated numbers?

I have a homework. The task is to decide, if the PRNG generated lottery is attackable/crackable or not. Details: Lottery: There is a lottery game where you have to choose 8 numbers between 1-20 for ...
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1answer
54 views

Can neural networks learn noise?

I'm interested in the following graphs. A neural network was trained to recognise digits from the MNIST dataset and then the labels were randomly shuffled and the following behaviour was observed. ...
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1answer
38 views

Are derived or computed inputs bad for CNNs?

I am building a CNN and am wondering if inputting derived or computed inputs are generally bad for the effectiveness of CNNs? Or just NNs in general? By derived or computed values I mean data that is ...
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Are there neural networks with (hard) constraints on the weights?

I don't know too much about Deep Learning, so my question might be silly. However, I was wondering whether there are NN architectures with some hard constraints on the weights of some layers. For ...
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41 views

What is the time complexity of DDPG algorithm?

Suppose we have a DDPG algorithm. The actor has N input nodes, two hidden layers with J nodes, and S output nodes. The critic has N+S input nodes, two hidden layers with C nodes, and one output node. ...
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1answer
65 views

Why is there tanh(x)*sigmoid(x) in a LSTM cell?

CONTEXT I was wondering why there are sigmoid and tanh activation functions in an LSTM cell. My intuition was based on the flow of tanh(x)*sigmoid(x) and the ...
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Which algorithms are used to locate objects in a 3d space?

I can see mobile apps that can locate a 3D object on a surface with a mobile camera and you can turn around that object. What is the name of the algorithm(s) that is used for that purpose? Or, is ...
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36 views

Use soft-max post-training for a ReLU trained network?

For a project, I've trained multiple networks for multiclass classification all ending with a ReLU activation at the output. Now the output logits are not probabilities. Is it valid to get the ...
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1answer
36 views

General approaches in text encoding and labelling for NLP [closed]

What are the approaches of encoding text data? I would be glad to hear some summarization from experienced persons. And are there any solutions accepting words outside the vocabulary and including ...
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26 views

Alternatives to neural networks for function approximation in Q learning?

I want to know if there is anything other than neural networks (or Deep NNs) that I can effectively use to perform function approximation? I am asking this w.r.t to the use of approximators in Q ...

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