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Disentangling feature and lazy training in deep neural networks

Three-dimensional tomography of red blood cells using deep learning

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Pedestrian Intention Prediction: A Multi-task Perspective

Detection of Bad PMU Data using Machine Learning Techniques

Biologically plausible deep learning – but how far can we go with shallow networks?

Incremental Learning Meets Reduced Precision Networks

The committee machine: computational to statistical gaps in learning a two-layers neural network

Jamming transition as a paradigm to understand the loss landscape of deep neural networks