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

Unable to meet desired mean squared error

I wish to get MSE < 0.5 on test data (https://easyupload.io/zr7xf3) which is 20% of given data chosen randomly. But I am reaching 0.73 using both plain Ridge Regression as well as a neural network ...
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14 views

Transforming neural network target values before training

Consider the scenario in which I am measuring certain $f(a,x)$, which i want to be the target value for some related input $g(a,x)$. In other words, I am trying to map $$g(a,x)\Rightarrow f(a,x)$$ I ...
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Different predictions across DL Frameworks [closed]

Can anyone give me a reason as to why I can train a neural network in say Tensorflow Flow, build equivalent models in pytorch and keras and any other DL framework, load the weights from the tensorflow ...
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81 views

Is it possible to perform neuroevolution without a fitness function?

My question is about neuroevolution (genetic algorithm + neural network): I want to create artificial life by evolving agents. But instead of relying on a fitness function, I would like to have the ...
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1answer
41 views

Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously?

Is there a common way to build a neural network that seeks to extract spatial and temporal information simultaneously? Is there an agreed up protocol on how to extract this information? What ...
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What is the best neural network model to classify an x(t) signal according two classes?

I am a beginner in AI methods. I have a collection of x(t) data, where x are some signal amplitudes and t is a time. My testing data are divided into two classes, say those from good and bad ...
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11 views

XOR problem with bipolar representation

I am taking a course in Machine Learning and the Professor introduced us to the XOR problem. I understand the XOR problem is not linearly separable and we need to employ Neural Network for this ...
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2answers
53 views

How is the error calculated with multiple output neurons in the neural network?

Machine Learning books generally explains that the error calculated for a given sample $i$ is: $e_i = y_i - \hat{y_i}$ Where $\hat{y}$ is the target output and $y$ is the actual output given by the ...
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How do LSTMs work if the following two matrices are not able to be multiplied?

In the above diagram, the shape of some of the matrices can be seen in the yellow highlight. For instance: The hidden state at timestep t-1 ($h_{t-1}$) has shape $(na, m)$ The input data at timestep t ...
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1answer
41 views

Improving DQN with fluctuations

Hello :) I'm pretty new to this community, so let me know if I posted anything incorrectly and I'll try to change it. I'm working on the project which aim is to create self-driving agent in CARLA. I ...
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how to handle highly imbalanced multilabel classification?

I am working on a multilabel classification in which I am having 206 labels. When I saw the percentage of the number of 1's in each label they are way less than 0.1% for each label. The maximum ...
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28 views

Dynamically adapting activation function

I am training a network through reinforcement learning. The policy network learns rotations, but depending on the actual input (state), the output of the network should be restricted to be in certain ...
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34 views

How efficient is SCAWI weight initialization method?

I'm currently in the middle of a project (for my thesis) constructing a deep neural network. Since I'm still in the research part, I'm trying to find various ways and techniques to initialize weights. ...
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1answer
190 views

Can the hidden layer prior to the ouput layer have less hidden units than the output layer?

I attended an introductory class about neural network and I had a question regarding how to choose the number of hidden units per hidden layer. I remember that the Professor saying that there is no ...
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How did they use their dataset with VAEs?

Old Photo Restoration via Deep Latent Space Translation (https://paperswithcode.com/paper/old-photo-restoration-via-deep-latent-space) In the article, it says : "We propose to restore old photos ...
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1answer
97 views

Smallest possible network to approximate the $sin$ function

The main goal is: Find the smallest possible neural network to approximate the $sin$ function. Moreover, I want to find a qualitative reason why this network is the smallest possible network. I have ...
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1answer
27 views

How to define Agar.io state and action space?

I am trying to implement an AI bot for my Agar.io clone using deep neural network. However, I am struggling with the state and action space of the AI bot. Because the bot can take real number for ...
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17 views

Wasserstein GAN with gradient penality - Loss values

I have trained a WAN with gradient penalty and the loss values ​​seem to me much higher than the examples I have seen on the net. The generator receives 2 images as input and must generate a ...
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19 views

Random Initializations with ReLU gives puzzling results

this may sound naive, but I’m getting a really puzzling result. I was experimenting with MNIST on vanilla MLP (784, 256, 128, 10) with ...
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30 views

Why do we have a sigmoid function in the input layer in LSTMs? [closed]

I'm particularly confused about the sigmoid function in the forget and input layer. If we use a sigmoid in the forget layer to look at $h_{t-1}$ and $x_{t}$, and output a number between 0 and 1 for ...
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1answer
62 views

What is the difference between neural networks and other ways of curve fitting?

For simplicity, let's assume we want to solve a regression problem, where we have one independent variable and one dependent variable, which we want to predict. Let's also assume that there is a ...
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1answer
38 views

Should the range and initial values of weights and biases be adjusted to fit input and output data?

As a routine (in typical everyday tasks) of a data scientist, should they usually decide about weights and biases range and initial values as a function of which data they are planning to insert as ...
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1answer
32 views

Evaluate model multiple times in loss function? Is this reinforcement learning?

I am interested in models that exhibit behavior. My goal is a model that survives indefinitely on a two dimensional resource landscape. One dimension represents the location (0 to 1) and the second ...
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21 views

Computer vision - Can you put more weight on a specific part of the object?

Let's say I'm looking for any item that has a certain shape (outline) in a photo. but I can further classify it only according to particular features, that most of them are expected to be shown only ...
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1answer
47 views

Is it possible to know the distance objects are from camera based on only knowing one object's height?

I am doing a project where I have to know distance a particular object is from camera. In the photo I only know one of the object's height, but I don't know how far away that object is and I don't ...
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29 views

Linear output layer back propagation

So I'm stack to something that it's probably very easy but I can't get my head around it. I'm building a Neural Network that will consist of many layers with non-linear activation functions (probably ...
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1answer
31 views

Computation of initial adjoint for NODE

I'm reading the paper Neural Ordinary Differential Equations and I have a simple question about adjoint method. When we train NODE, it uses a blackbox ODESolver to compute gradients through model ...
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2answers
64 views

What happens when an opponent a neural network is playing with does not obey the rules of the game (i.e. cheats)?

For example, if AlphaZero plays with an opponent who has a right to move chess figures any way she wants, or make more than 1 move in a turn? Will a neural network adapt to that, as it adapted to an ...
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2answers
67 views

What is the advantage of using cross entropy loss & softmax?

I am trying to do the standard MNIST dataset image recognition test with a standard feed forward NN, but my network failed pretty badly. Now I have debugged it quite a lot and found & fixed some ...
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32 views

When do the ensemble methods beat Neural Networks?

In many applications and domains : Computer Vision, Natural Language Processsing, Image Segmentation, and many other tasks - neural networks of a certain architecture are considered to be by far the ...
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1answer
80 views

When are multiple hidden layers necessary?

I know that my question probably seems like being asked many times, but Ill try to be more speciffic: Limitations to my question: I am NOT asking about convolutional neural networks, so please, try ...
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1answer
26 views

How to train the NN of simple agents given a reward system?

I'm not an expert in AI or NN, I gathered most of the information I have from the internet, and I'm looking for advice and guidance. I'm trying to design a NN that is going to be used by all the ...
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9 views

Cocatenate feature extractor layers with different channels

I have a network architecture for feature extraction and I wanted to concatenate layers of the same dimensions but with different feature channels. ...
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21 views

Choice of loss function for semantic segmentation

I am training a U-Net for semantic segmentation of large medical images (4096x4096px). The two classes are "too" unbalanced. The white pixels are just about 0.1% (or less) of the whole image....
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1answer
40 views

What is the status of the capsule networks?

What is the status of the capsule networks? I got an impression that capsule networks turned out not to be so useful in applications more complicated than the MNIST (at least according to this reddit ...
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29 views

Rules of thumb for hidden layer sizes [duplicate]

I am quite new to neural networks, and would like to save myself some of the learning curve by having some rules of thumb about hidden layer sizes. I would also like to have a rule of thumb for the ...
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24 views

How to convert a certain file format into another using neural network?

I am an amateur with codes but still interested by ML and NN applications. How would I go to convert one file format to another by using neural network. e.g: mp3 to wav, fbx to obj, wav to png, ...
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44 views

Is it better to split sequences into overlapping or non-overlapping training samples?

I have $N$ (time) sequences of data with length $2048$. Each of these sequences correseponds to a different target output. However, I know that only a small part of the sequence is needed to actually ...
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1answer
81 views

What is the loss for policy gradients with continuous actions?

I know with policy gradients used in an environment with a discrete action space are updated with $$ \Delta \theta_{t}=\alpha \nabla_{\theta} \log \pi_{\theta}\left(a_{t} \mid s_{t}\right) v_{t} $$ ...
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1answer
63 views

Why neural networks tend to be trained to recognize multiple things instead of just one?

I was watching this series: https://www.youtube.com/watch?v=aircAruvnKk The series demonstrates neural networks by building a simple number recognizing network. It got me thinking: Why traditionally ...
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24 views

Training a CNN for semantic segmentation of large 4600x4600px images

I am trying to implement a CNN (U-Net) for semantic segmentation of similar large grayscale ~4600x4600px medical images. The area I want to segment is the empty space (gap) between a round object in ...
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1answer
49 views

Chess Neural Network - Most Optimal Input vector/matrix?

I'm wanting to build a NN that can create a policy for each possible state. I want to combine this with MCTS to eliminate randomness so when expansion occurs, I can get the probability of the move to ...
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1answer
66 views

Is it legal to license and sell the output of a neural network that was trained on data that you don't own the license to?

Is it legal to license and sell the output of a neural network that was trained on data that you don't own the license to? For example, suppose you trained WaveNet on a collection of popular music. ...
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1answer
39 views

Comparing a large/general CNN to a smaller more specialized one?

I am still somewhat a novice in the ML world, but I had a strange idea about CNNs and wanted to ask if this would be a valid way to check the robustness of a general CNN that classifies certain images....
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14 views

Ignoring model testing at neural networks

I've already collected a small dataset to estimate neural networks model for prediction purposes. My question is skipping the testing stage at neural networks such as General Regression Neural Network ...
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16 views

Why is sine activation function not used frequently since we know from fourier transforms that sine functions can combine to fit any function?

Pretty much the title. I'm no expert but from what I know, if you add up enough sine functions with proper amplitudes and frequencies you can get any function you want as a result. With that knowledge,...
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17 views

Neural network algorithm implementation for Iris dataset

I want to use Neural network algorithm over famous Iris dataset. Iris dataset attributes names sepal length in cm sepal width in cm petal length in cm petal width in cm Sample dataset: ...
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1answer
38 views

How does a neural network that has been trained keep learning while in a real world scenario

Say I trained a Neural Network (not RNN or CNN) to classify a particular data set. So I train using a specific data set & then I test using another and get an accuracy of 95% which is good enough. ...
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1answer
20 views

Flatten image using Neural network and matrix transpose

I have read a lecture note of Prof. Andrew Ng. There was something about data normalization like how can we flatten an image of (64x64x3) into a (64x64x3)*x1 vector. After that there is pictorial ...
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1answer
45 views

Is this ML task possible?

What I want to do is from an Internet challenge to transform any given image into the Polish flag using the available filters and crop tool on the iPhone camera app. Here's an example. There aren't ...

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