Questions tagged [neural-architecture-search]

For questions related to the concept of neural (network) architecture search (NAS), which is a way of automating the design (that is, the hyper-parameters) of a neural network. NAS is related to neuroevolution, given that neuroevolution can be used to perform NAS, but neuroevolution is not the only way of performing NAS. For example, reinforcement learning can also be used to perform NAS.

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What Deep Learning Applications Might Require Super-Computers or “SuperPODs”

With the release of NVIDIA's DGX SuperPOD of A100 GPUs, supercomputers will/are becoming more and more common-place. What potential deep learning tasks/applications might become more accessible with ...
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Need to determine a ML solution for the given graphical problem

I need to generate a 3D plane given a set of feature inputs. Most inputs are a range of values between 0 and 1 (sigmoidial), except a few. For example a rectangle: ...
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Can operations like convolution and pooling be discovered with a neural architecture search approach?

From Neural Architecture Search: A Survey, first published in 2018: Moreover, common search spaces are also based on predefined building blocks, such as different kinds of convolutions and ...
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Autoencoder symmetry

I'm building Autoencoder. In the encoding part I use several strided and diluted convolutions per stage of encoding but I'm wondering if I have to construct the decoding part in a symmetric way. My ...
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How do I determine the best neural network architecture for a problem with 3 inputs and 12 outputs?

This post continues the topic in the following post: Is it possible to train a neural network with 3 inputs and 12 outputs?. I conducted several experiments in MATLAB and selected those neural ...
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Is it possible to train a neural network with 3 inputs and 12 outputs?

The selection of experimental data includes a set of vectors of different dimensions. The input is a 3-dimensional vector, and the output is a 12-dimensional vector. The sample size is 120 pairs of ...
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Regional specialization in neural networks (especially for language processing)?

What is the status of the research on regional specialization of the artificial neural networks? Biology knows that such specialization exists in the brain and it is very important for the functioning ...
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Number of weights in historical to cutting edge deployment of deep networks [closed]

In cutting edge deployment of deep networks for different architectures (such as $CNN$, $QRNN$ etc) what is the historical trend of current limits of trainability possible computationally? By this I ...
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Convolutional filters: create new ones

I'm studying a Master's Degree in Artificial Intelligence an my final work is about Convolutional Neuronal Networks. I was looking for information about filters (or kernel) at the convolutional ...
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Which hyper-parameters are considered in neural architecture search?

I want to understand automatic Neural Architecture Search (NAS). I read already multiple papers, but I cannot figure out what the actual search space of NAS is / how are classical hyper-parameters ...
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Which hyperparameter does Neural Architecture search (NAS) use?

i have read a lot about NAS but I still do not understand one concept: When setting up a neural network, hyperparameters need to set up, like for example the learning rate, dropout rate, batch size, ...
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Can neural networks modify their own weights without back-propagation and gradient descent?

Can neural networks modify their own weights without back-propagation and gradient descent?
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How does RL based neural architecture search work?

I have read through many of the papers and articles linked in this thread but I haven't been able to find an answer to my question. I have built some small RL networks and I understand how REINFORCE ...
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How is neural architecture search performed?

I have come across something that IBM offers called neural Architecture search. You feed it a data set and it outputs an initial neural Architecture that you can train. How is neural architecture ...