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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13 views

How would I preprocess images data for training in a Graph Convolutional Network (GCN) model? [closed]

I have images data, but I can not understand how I feed the data into the GCN model and get the accuracy. Please help me out!
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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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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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26 views

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
26 views

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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1answer
26 views

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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31 views

Question about back-propagation derivation and code implementation [closed]

Could anyone advise about how to mathematically obtain the following partial derivatives expressions for grad_W1, grad_W2, grad_b1, grad_b2 ? ...
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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?

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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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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1answer
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
55 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
54 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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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
27 views

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
66 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
43 views

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
37 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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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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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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27 views

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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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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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
25 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
37 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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51 views

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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40 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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56 views

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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0answers
60 views

How to estimate conditional density using neural network?

Conditional Variational Autoencoders (CVAE) and Mixture Density Networks (MDN) are supposed to address this issue. However, these models provide the distribution parameters, e.g., mean and standard ...
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144 views

What parameters or hyper-parameters of my model for time-series should I change to improve the MAE?

The following time series exercise is about writing the best possible model, minimizing the MAE. Helper functions normalize_series, ...

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