99 citations · 347 across the 39 of their papers we have counts for
41 papers
Improving robustness to corruptions with multiplicative weight perturbations
Trung Trinh, Markus Heinonen, Luigi Acerbi +1
Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones. Incorporating specific corruptions into the data augmentation pipeline can improve robustness to…
From Alexnet to Transformers: Measuring the Non-linearity of Deep Neural Networks with Affine Optimal Transport
Quentin Bouniot, Ievgen Redko, Anton Mallasto +6
In the last decade, we have witnessed the introduction of several novel deep neural network (DNN) architectures exhibiting ever-increasing performance across diverse tasks. Explain…
Learning Space-Time Continuous Neural PDEs from Partially Observed States
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
We introduce a novel grid-independent model for learning partial differential equations (PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a s…
Input-gradient space particle inference for neural network ensembles
Trung Trinh, Markus Heinonen, Luigi Acerbi +1
Deep Ensembles (DEs) demonstrate improved accuracy, calibration and robustness to perturbations over single neural networks partly due to their functional diversity. Particle-based…
AbODE: Ab Initio Antibody Design using Conjoined ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Antibodies are Y-shaped proteins that neutralize pathogens and constitute the core of our adaptive immune system. De novo generation of new antibodies that target specific antigens…
Learning representations that are closed-form Monge mapping optimal with application to domain adaptation
Oliver Struckmeier, Ievgen Redko, Anton Mallasto +3
Optimal transport (OT) is a powerful geometric tool used to compare and align probability measures following the least effort principle. Despite its widespread use in machine learn…