Deep Spline Networks With Control Of Lipschitz Regularity

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2

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The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real -time and many-query evaluations of parametric ...

Deep Spline Networks With Control Of Lipschitz Regularity

Chattez avec Graph Search

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Safe Deep Neural Networks

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Generalization of Scaled Deep ResNets in the Mean-Field Regime

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Error assessment of an adaptive finite elements-neural networks method for an elliptic parametric PDE

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Machine Learning for Modeling Stock Returns

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

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Random matrix methods for high-dimensional machine learning models

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

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M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable Systems

Topics in statistical physics of high-dimensional machine learning

A graph convolutional autoencoder approach to model order reduction for parametrized PDEs