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

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

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Three-dimensional tomography of red blood cells using deep learning

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Multi-memristive synaptic architectures for training neural networks

Real-time, low-latency closed-loop feedback using markerless posture tracking

Real-time, low-latency closed-loop feedback using markerless posture tracking

Learning Activation Functions in Deep (Spline) Neural Networks

Deep learning-based reduced order models in cardiac electrophysiology

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification

Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Real-time, low-latency closed-loop feedback using markerless posture tracking

Multi-memristive synaptic architectures for training neural networks

Reservoir Computing meets Recurrent Kernels and Structured Transforms

Real-time, low-latency closed-loop feedback using markerless posture tracking

Learning Multi-Label Aerial Image Classification Under Label Noise: A Regularization Approach Using Word Embeddings

Three-dimensional tomography of red blood cells using deep learning

Learning Activation Functions in Deep (Spline) Neural Networks

Deep learning-based reduced order models in cardiac electrophysiology

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification