Publications related to Measuring Robustness of Classifiers to Geometric Transformations

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

In this thesis, we propose new algorithms to solve inverse problems in the context of biomedical images. Due to ill-posedness, solving these problems require some prior knowledge of the statistics of the underlying images. The traditional algorithms, in th ...

EPFL2020

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

Geometry of adversarial robustness of deep networks: methods and applications

Seyed Mohsen Moosavi Dezfooli

We are witnessing a rise in the popularity of using artificial neural networks in many fields of science and technology. Deep neural networks in particular have shown impressive classification performance on a number of challenging benchmarks, generally in ...

EPFL2019

A geometry-inspired decision-based attack

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Yujia Liu

Deep neural networks have recently achieved tremen-dous success in image classification. Recent studies havehowever shown that they are easily misled into incorrectclassification decisions by adversarial examples. Adver-saries can even craft attacks by que ...

2019

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Michaël Unser, Michael Thompson McCann

This tutorial covers biomedical image reconstruction, from the foundational concepts of system modeling and direct reconstruction to modern sparsity and learning-based approaches. Imaging is a critical tool in biological research and medicine, and most ima ...

2019

Incremental Learning Meets Reduced Precision Networks

Hardware accelerators for Deep Neural Networks (DNNs) that use reduced precision parameters are more energy efficient than the equivalent full precision networks. While many studies have focused on reduced precision training methods for supervised networks ...

2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

Biologically plausible deep learning – but how far can we go with shallow networks?

Wulfram Gerstner, Johanni Michael Brea, Bernd Albert Illing

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

SparseFool: a few pixels make a big difference

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Apostolos Modas

Deep Neural Networks have achieved extraordinary results on image classification tasks, but have been shown to be vulnerable to attacks with carefully crafted perturbations of the input data. Although most attacks usually change values of many image's pixe ...

IEEE2019

Copper-Catalyzed Oxyalkynylation of C-S Bonds in Thiiranes and Thiethanes with Hypervalent Iodine Reagents

Jérôme Waser, Guillaume Dominique Pisella, Julien Aymeric Borrel

We report the oxyalkynylation of thiiranes and thietanes using ethynylbenziodoxolone reagents (EBXs) to readily access functionalized building blocks bearing an alkynyl, a benzoate, and an iodide group. The reaction proceeds with high atom efficiency most ...

2019

Measuring Robustness of Classifiers to Geometric Transformations

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From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

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

Geometry of adversarial robustness of deep networks: methods and applications

A geometry-inspired decision-based attack

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Incremental Learning Meets Reduced Precision Networks

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Biologically plausible deep learning – but how far can we go with shallow networks?

SparseFool: a few pixels make a big difference

Copper-Catalyzed Oxyalkynylation of C-S Bonds in Thiiranes and Thiethanes with Hypervalent Iodine Reagents

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

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

From Classical to Unsupervised-Deep-Learning Methods for Solving Inverse Problems in Imaging.

A geometry-inspired decision-based attack

Geometry of adversarial robustness of deep networks: methods and applications

Biomedical Image Reconstruction: From the Foundations to Deep Neural Networks

Incremental Learning Meets Reduced Precision Networks

Biologically plausible deep learning – but how far can we go with shallow networks?

SparseFool: a few pixels make a big difference

Copper-Catalyzed Oxyalkynylation of C-S Bonds in Thiiranes and Thiethanes with Hypervalent Iodine Reagents