Body: <p>I am confused by this paper <a href="https://arxiv.org/pdf/1410.0759.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1410.0759.pdf</a> which displays on page 4 how to model a 3D convolution (input has more than 1 channel and filter has more than one output).</p>
  <p>In this example, each filter kernel of one output dimension is reshaped into one single row.</p>
  <p>The input channels are stacked above each other where a sliding window with the size of the filter defines the content of one row.</p>
  <p>In this example, the number of columns of the filter matrix and the number of rows of the input matrix are equal, but this doesn't have to be.</p>
  <p>Let's say the input data are 4x4 blocks - this means the input matrix still has 4 columns (because the filter is 2x2) but now has 9 x 3 = 27 rows which is more than the 12 columns of the filter matrix.</p>
  <p>This doesn't make sense to me?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-20T09:13:30.823Z
Comments.Text: Can you please **explicitly** ask a **specific question**, which you should also put in the title? I don't see any question here. The last sentence is not really a question that we can answer.
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-05-23T13:20:29.353Z
Comments.Text: It would be best if you could give a specific example and then ask a specific or multiple specific questions. I'm not sure what you mean.
Comments.UserDisplayName: emely_pi
Tags: <machine-learning><implementation><convolution>
Title: Implement 4D convolution as matrix-matrix multiplication - paper is confusing!
UserDisplayName: binaryBigInt
ThreadText: # Implement 4D convolution as matrix-matrix multiplication - paper is confusing!
  
  (Asked by: username_0 on 2022-05-17)
  
  I am confused by this paper <https://arxiv.org/pdf/1410.0759.pdf> which displays on page 4 how to model a 3D convolution (input has more than 1 channel and filter has more than one output).
  
  In this example, each filter kernel of one output dimension is reshaped into one single row.
  
  The input channels are stacked above each other where a sliding window with the size of the filter defines the content of one row.
  
  In this example, the number of columns of the filter matrix and the number of rows of the input matrix are equal, but this doesn't have to be.
  
  Let's say the input data are 4x4 blocks - this means the input matrix still has 4 columns (because the filter is 2x2) but now has 9 x 3 = 27 rows which is more than the 12 columns of the filter matrix.
  
  This doesn't make sense to me?
  
  **Tags:** machine-learning, implementation, convolution
  
  > **Comments:**
  > **username_1**: Can you please **explicitly** ask a **specific question**, which you should also put in the title? I don't see any question here. The last sentence is not really a question that we can answer.  
  > **username_2**: It would be best if you could give a specific example and then ask a specific or multiple specific questions. I'm not sure what you mean.  
  
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  ## 0 answers
  
  