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  • What is the difference between Conv1D and Conv2D?
    I will be using a Pytorch perspective, however, the logic remains the same When using Conv1d (), we have to keep in mind that we are most likely going to work with 2-dimensional inputs such as one-hot-encode DNA sequences or black and white pictures The only difference between the more conventional Conv2d () and Conv1d () is that latter uses a 1-dimensional kernel as shown in the picture
  • Difference between Conv and FC layers? - Cross Validated
    What is the difference between conv layers and FC layers? Why cannot I use conv layers instead of FC layers?
  • definition of hidden unit in a ConvNet - Cross Validated
    Generally speaking, I think for conv layers we tend not to focus on the concept of 'hidden unit', but to get it out of the way, when I think 'hidden unit', I think of the concepts of 'hidden' and 'unit' For me, 'hidden' means it's neither something in the input layer (the inputs to the network), or the output layer (the outputs from the network) A 'unit' to me is a single output from a
  • How does applying a 1-by-1 convolution (bottleneck layer) between conv . . .
    How does applying a 1-by-1 convolution (bottleneck layer) between conv layers change the output? [duplicate] Ask Question Asked 5 years, 11 months ago Modified 1 year, 2 months ago
  • What are the advantages of FC layers over Conv layers?
    I am trying to think of scenarios where a fully connected (FC) layer is a better choice than a convolution layer In terms of time complexity, are they the same? I know that convolution can represe
  • Understanding the function of attention layers in a convolutional . . .
    I am trying to understand the neural network architecture used by Ho et al in quot;Denoising Diffusion Probabilistic Models quot; (paper, source code) They include self-attention layers in the m
  • Convolutional Layers: To pad or not to pad? - Cross Validated
    If the CONV layers were to not zero-pad the inputs and only perform valid convolutions, then the size of the volumes would reduce by a small amount after each CONV, and the information at the borders would be “washed away” too quickly " -
  • Where should I place dropout layers in a neural network?
    I've updated the answer to clarify that in the work by Park et al , the dropout was applied after the RELU on each CONV layer I do not believe they investigated the effect of adding dropout following max pooling layers





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