Publications related to Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

Multi-memristive synaptic architectures for training neural networks

Highly data-centric AI workloads require that new computing paradigms be adopted because the performance of traditional CPU- and GPU-based systems are limited by data access and transfer. Training deep neural networks with millions of tunable parameters ta ...

EPFL2020

A deep learning framework for matching of SAR and optical imagery

Devis Tuia, Sylvain Lobry, Lloyd Haydn Hughes

SAR and optical imagery provide highly complementary information about observed scenes. A combined use of these two modalities is thus desirable in many data fusion scenarios. However, any data fusion task requires measurements to be accurately aligned. Wh ...

2020

Deep learning-based reduced order models in cardiac electrophysiology

Alfio Quarteroni, Andrea Manzoni

Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depola ...

PUBLIC LIBRARY SCIENCE2020

Comparison of crack segmentation using digital image correlation measurements and deep learning

Katrin Beyer, Radhakrishna Achanta, Amir Rezaie, Michele Godio

Reliable methods for detecting pixels that represent cracks from laboratory images taken for digital image correlation (DIC) are required for two main reasons. Firstly, the segmented crack maps are used as an input for some DIC methods that are based on di ...

2020

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Ming Jiang, Cheng Shi

Neuron detection is a key step in individualizing and counting neurons which are important for assessing physiological and pathophysiological information. A large number of methods including deep learning networks have been proposed but mainly targeting re ...

IEEE2020

Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability

Devis Tuia

Machine learning and deep learning in particular have made a huge impact in many fields of science and engineering. In the last decade, advanced deep learning methods have been developed and applied to remote sensing and geoscientific data problems extensi ...

2020

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Thierry Bossy

Introduction: The unprecedented speed and scale of the COVID-19 pandemic necessitated the rapid implementation of untested public health measures to mitigate the consequences of viral spread. In the 8 months that have passed since the first recognized case ...

2020

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Alexander Mathis, Mackenzie Mathis, Steffen Schneider, Jessy Lauer

Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced our ability to predict posture directly from videos, which has quickly ...

2020

Domain-Adaptive Multibranch Networks

Pascal Fua, Mathieu Salzmann, Róger Bermúdez Chacón

We tackle unsupervised domain adaptation by accounting for the fact that different domains may need to be processed differently to arrive to a common feature representation effective for recognition. To this end, we introduce a deep learning framework wher ...

2020

Square Attack: a query-efficient black-box adversarial attack via random search

Nicolas Henri Bernard Flammarion, Maksym Andriushchenko, Francesco Croce

We propose the Square Attack, a new score-based black-box

l_2

and

l_\infty

adversarial attack that does not rely on local gradient information and thus is not affected by gradient masking. The Square Attack is based on a randomized search scheme where ...

2020

Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness

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Multi-memristive synaptic architectures for training neural networks

A deep learning framework for matching of SAR and optical imagery

Deep learning-based reduced order models in cardiac electrophysiology

Comparison of crack segmentation using digital image correlation measurements and deep learning

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Domain-Adaptive Multibranch Networks

Square Attack: a query-efficient black-box adversarial attack via random search

A Primer on Motion Capture with Deep Learning: Principles, Pitfalls, and Perspectives

Multi-memristive synaptic architectures for training neural networks

A deep learning framework for matching of SAR and optical imagery

Advancing Deep Learning for Earth Sciences: From Hybrid Modeling to Interpretability

Comparison of crack segmentation using digital image correlation measurements and deep learning

Adaptive Mitigation: Identification of the Dynamic Drivers of Effective Policy during the COVID-19 Pandemic

Deep learning-based reduced order models in cardiac electrophysiology

Automated Detection Of Highly Aggregated Neurons In Microscopic Images Of Macaque Brain

Domain-Adaptive Multibranch Networks

Square Attack: a query-efficient black-box adversarial attack via random search