
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deform...
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Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
A commonly cited inefficiency of neural network training using backprop...
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Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of LowPhoton Nanoscale Imaging
Phase retrieval is the inverse problem of recovering a signal from magni...
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Decoupled Greedy Learning of CNNs
A commonly cited inefficiency of neural network training by backpropaga...
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Greedy Layerwise Learning Can Scale to ImageNet
Shallow supervised 1hidden layer neural networks have a number of favor...
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Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation s...
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Solid Harmonic Wavelet Scattering for Predictions of Molecule Properties
We present a machine learning algorithm for the prediction of molecule p...
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FAASTA: A fast solver for totalvariation regularization of illconditioned problems with application to brain imaging
The total variation (TV) penalty, as many other analysissparsity proble...
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Machine Learning for Neuroimaging with ScikitLearn
Statistical machine learning methods are increasingly used for neuroimag...
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Second order scattering descriptors predict fMRI activity due to visual textures
Second layer scattering descriptors are known to provide good classifica...
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Michael Eickenberg
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