To provide dimensionality reduction, 1x1 convolutions are used, before passsing them through a 3x3, or 5x5 convolution in an Inception module.
3809
What does 1x1 convolution mean in a neural network?
136698
Why are there different ways of building projection matrices?
1756
1-point compactification and embedding
59598
The vectors are orthonormal
155450
Neural Network: MLP for regression with 3 continuous features, 1 categorical
138904
Recursive feature elimination and one-hot & dummy encoding?
15759
Adding Regressors to Convolutional Neural Network
6914
Why are neural networks becoming deeper, but not wider?
18487
Why are there only $n-1$ principal components for $n$ data if the number of dimensions is $\ge n$?
157062
Dimensionality reduction (SVD or PCA) on a large, sparse matrix
127285
Inverse images of images, images of inverse images of a set of vectors
137086
Can Convolution Neural Network be useful with encoded categorical features?
108939
Definition of convolution?
149875
Why does increasing the number of features reduce performance?
126800
Why do we use multiple epochs and why does it not lead to over fitting
60602
Is the convolution operation some kind of group operation?
43480
Visualizing the 4th dimension.