A Kernel Perspective for Regularizing Deep Neural Networks

74 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Diversity With Cooperation: Ensemble Methods for Few-Shot Classification (2019, methodology): “Cooperation encourages the non-ground truth probabilities (in red) to be similar, after normalization, whereas diversity encourages orthogonality. deep networks [2, 35], we choose in this paper to focus on variance-reduction principles based on ensemble methods.”
Group Invariance, Stability to Deformations, and Complexity of Deep Convolutional Representations (2017, background): “Using such quantities to regularize during training may then yield bounds that are less vacuous (Bietti et al., 2019).”
Truth or Backpropaganda? An Empirical Investigation of Deep Learning Theory (2019, background): “The wide network limit and Gaussian process interpretations have inspired work on the neural tangent kernel (Jacot et al., 2018), while Lee et al. (2019) and Bietti et al. (2018) have used wide network assumptions to analyze the training dynamics of deep networks.”