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Found 8 papers, 4 papers with code
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis.
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods.
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, obtaining accuracies in the range of 87-90% while being more amenable to theoretical analysis.
Diptychs of human and machine perceptions
We propose visual creations that put differences in algorithms and humans \emph{perceptions} into perspective.
Interactive Neural Style Transfer with Artists
We present interactive painting processes in which a painter and various neural style transfer algorithms interact on a real canvas.
Deep Network Classification by Scattering and Homotopy Dictionary Learning
It is implemented in a deep convolutional network with a homotopy algorithm having an exponential convergence.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule properties inspired by ideas from density functional theory.
