31 citations · 47 across the 10 of their papers we have counts for
5 papers · 1 filter
EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering
Nicolas Deutschmann, Constance Ferragu, Jonathan D. Ziegler +2
We introduce EvoFlows, a variable-length protein sequence-to-sequence modeling approach designed for protein engineering. Existing protein language models are poorly suited for opt…
Measuring Complexity of Learning Schemes Using Hessian-Schatten Total Variation
Shayan Aziznejad, Joaquim Campos, Michael Unser
In this paper, we introduce the Hessian-Schatten total variation (HTV) -- a novel seminorm that quantifies the total "rugosity" of multivariate functions. Our motivation for defini…
Sparsest Univariate Learning Models Under Lipschitz Constraint
Shayan Aziznejad, Thomas Debarre, Michael Unser
Beside the minimization of the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability. Driven by these principles, we prop…
Deep Neural Networks with Trainable Activations and Controlled Lipschitz Constant
Shayan Aziznejad, Harshit Gupta, Joaquim Campos +1
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…
Multi-Kernel Regression with Sparsity Constraint
Shayan Aziznejad, Michael Unser
In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that ar…