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Regularization Networks for Inverse Problems: A state-space approach

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Regularization Networks for Inverse Problems: A state-space approach

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

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

Deep Learning Generalization with Limited and Noisy Labels

Benign Overfitting in Deep Neural Networks under Lazy Training

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Leveraging Unlabeled Data to Track Memorization

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

A Neural-Network-Based Convex Regularizer for Inverse Problems

Regularization of polynomial networks for image recognition

Regularization of polynomial networks for image recognition

Fundamental Limits in Statistical Learning Problems: Block Models and Neural Networks

Regularization of polynomial networks for image recognition

Deep Learning Generalization with Limited and Noisy Labels

A Neural-Network-Based Convex Regularizer for Inverse Problems

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

Leveraging Unlabeled Data to Track Memorization

Regularization of polynomial networks for image recognition

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

From Kernel Methods to Neural Networks: A Unifying Variational Formulation

Benign Overfitting in Deep Neural Networks under Lazy Training