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Physics-informed machine learning for reduced-order modeling of nonlinear problems

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Physics-informed machine learning for reduced-order modeling of nonlinear problems

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Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Disentangling feature and lazy training in deep neural networks

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Crowding and the Architecture of the Visual System

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Capsule networks explain complex spatial processing

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

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

Multimode optical fiber transmission with a deep learning network

Crowding and the Architecture of the Visual System

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

How single neuron properties shape chaotic dynamics and signal transmission in random neural networks

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Capsule networks explain complex spatial processing

Multimode optical fiber transmission with a deep learning network

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Recurrent neural network closure of parametric POD-Galerkin reduced-order models based on the Mori-Zwanzig formalism

Disentangling feature and lazy training in deep neural networks