Joaquim Gonçalves Garcia Barreto Campos

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Related publications (5)

Stable parameterization of continuous and piecewise-linear functions

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Alexis Marie Frederic Goujon

Rectified-linear-unit (ReLU) neural networks, which play a prominent role in deep learning, generate continuous and piecewise-linear (CPWL) functions. While they provide a powerful parametric representation, the mapping between the parameter and function s ...

2023

Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

2023

Learning of Continuous and Piecewise-Linear Functions With Hessian Total-Variation Regularization

Michaël Unser, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

We develop a novel 2D functional learning framework that employs a sparsity-promoting regularization based on second-order derivatives. Motivated by the nature of the regularizer, we restrict the search space to the span of piecewise-linear box splines shi ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Deep Neural Networks With Trainable Activations and Controlled Lipschitz Constant

Michaël Unser, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad

We introduce a variational framework to learn the activation functions of deep neural networks. Our aim is to increase the capacity of the network while controlling an upper-bound of the actual Lipschitz constant of the input-output relation. To that end, ...

2020

Learning Activation Functions in Deep (Spline) Neural Networks

Michaël Unser, Harshit Gupta, Joaquim Gonçalves Garcia Barreto Campos, Shayan Aziznejad, Pakshal Narendra Bohra

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the ...

2020

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