
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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The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
A recent line of work showed that various forms of convolutional kernel ...
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Interferometric Graph Transform: a Deep Unsupervised Graph Representation
We propose the Interferometric Graph Transform (IGT), which is a new cla...
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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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Compressing the Input for CNNs with the FirstOrder Scattering Transform
We study the firstorder scattering transform as a candidate for reducin...
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Scattering Networks for Hybrid Representation Learning
Scattering networks are a class of designed Convolutional Neural Network...
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Nonlinear Acceleration of CNNs
The Regularized Nonlinear Acceleration (RNA) algorithm is an acceleratio...
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Nonlinear Acceleration of Deep Neural Networks
Regularized nonlinear acceleration (RNA) is a generic extrapolation sche...
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iRevNet: Deep Invertible Networks
It is widely believed that the success of deep convolutional networks is...
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Scaling the Scattering Transform: Deep Hybrid Networks
We use the scattering network as a generic and fixed initialization of ...
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Multiscale Hierarchical Convolutional Networks
Deep neural network algorithms are difficult to analyze because they lac...
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Building a Regular Decision Boundary with Deep Networks
In this work, we build a generic architecture of Convolutional Neural Ne...
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Deep RotoTranslation Scattering for Object Classification
Dictionary learning algorithms or supervised deep convolution networks h...
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Generic Deep Networks with Wavelet Scattering
We introduce a twolayer wavelet scattering network, for object classifi...
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Edouard Oyallon
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