Detailed Instructions: In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.
Q: Recent work has focused on combining kernel methods and deep learning. With this in mind, we introduce Deepstrom networks -- a new architecture of neural networks which we use to replace top dense layers of standard convolutional architectures with an approximation of a kernel function by relying on the Nystrom approximation.
Our approach is easy highly flexible. It is compatible with any kernel function and it allows exploiting multiple kernels.
We show that Deepstrom networks reach state-of-the-art performance on standard datasets like SVHN and CIFAR100. One benefit of the method lies in its limited number of learnable parameters which make it particularly suited for small training set sizes, e.g. from 5 to 20 samples per class. Finally we illustrate two ways of using multiple kernels, including a multiple Deepstrom setting, that exploits a kernel on each feature map output by the convolutional part of the model.
A:
A new neural architecture where top dense layers of standard convolutional architectures are replaced with an approximation of a kernel function by relying on the Nystrom approximation.