Questions tagged [memory]
The memory tag has no usage guidance.
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Is batch size of 1 a valid choice for a very deep neural network with high memory requirement?
I am training a very deep neural network (Panoptic-DeepLab) with a ResNet34 backbone on Google Colab on CityScapes dataset for Panoptic Segmentation, and noticed that, with a big crop size, the batch ...
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Transformer-XL query length differs during inference and optimization?
I'm working through KakaoBrain's Transformer Reinforcement Learning implementation.
https://github.com/kakaobrain/brain_agent
I observed that the query length during sampling is set to ...
- 101
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Comparing large numbers of images to find outliers
There are many methods you can use to compare two images in ML (Siamese NN, CNNs, Ect.) What I cannot figure out is comparing a large number of images (Without Retraining) to find images of a ...
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What is the bit memory task?
I learned from this post about the so-called bit memory:
They froze its self-attention and feed-forward layers and, in separate copies, fine-tuned peripheral layers on each on a wide range of tasks: ...
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How to calculate the GPU memory need to run a deep learning network?
In general, how do I calculate the GPU memory need to run a deep learning network?
I'm asking this question because my training for some network configuration is getting out of memory.
If the ...
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How does the memory mechanism (reading and writing) work in a neural Turing machine?
In neural Turing machine (NTM), reading memory is represented as
\begin{align}
r_t \leftarrow \sum\limits_i^R w_t(i) \mathcal{M}_t(i) \tag{2}
\end{align}
and writing to memory is represented as
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How much Physical memory does Alpha Zero's Neural Net require?
I appreciate that there are many ways to arrange the memory in a NN, and that the numerical representations may be with bytes to floats depending on an implementation. What is typical amount of ...
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How to manage large amounts of image data for training?
Right now, I am trying to synthesize training images for a CNN and due to the nature of the application, there is a finite number of sample images to learn from.
From other research, I expect to be ...
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Does a data structure that models the encoding specificity principle in memory exist?
I find myself wondering if there exists a data structure with the following properties:
Stores information
Conforms to the encoding specificity principle
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How can a specific connectivity pattern be stored in an optimally compact representation?
I am interested in optimizing the memory capacity of an AGI. Given a specific complex input an AI can create a simplified model. This is a problem that can be solved using sparse coding [1]. However, ...
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