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Improving the Training of Compact Neural Networks for Visual Recognition

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Improving the Training of Compact Neural Networks for Visual Recognition

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Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Confidence Matters: Applications to Semantic Segmentation

Supervised learning and inference of spiking neural networks with temporal coding

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

An exact mapping from ReLU networks to spiking neural networks

How deep convolutional neural networks lose spatial information with training

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

Supervised learning and inference of spiking neural networks with temporal coding

3D reconstruction of curvilinear one-dimensional objects viewed in transmission electron microscopy

How deep convolutional neural networks lose spatial information with training

Regularization of polynomial networks for image recognition

BLACK-BOX ATTACKS ON IMAGE ACTIVITY PREDICTION AND ITS NATURAL LANGUAGE EXPLANATIONS

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Confidence Matters: Applications to Semantic Segmentation

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

An exact mapping from ReLU networks to spiking neural networks

Regularization of polynomial networks for image recognition