Publications related to Confidence Matters: Applications to Semantic Segmentation

Crowding and the Architecture of the Visual System

Classically, vision is seen as a cascade of local, feedforward computations. This framework has been tremendously successful, inspiring a wide range of ground-breaking findings in neuroscience and computer vision. Recently, feedforward Convolutional Neural ...

EPFL2020

The Unstoppable Rise of Computational Linguistics in Deep Learning

James Henderson

In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures. We focus on the importance of v ...

Association for Computational Linguistics2020

Three-dimensional tomography of red blood cells using deep learning

Demetri Psaltis, Joowon Lim, Ahmed Ayoub

We accurately reconstruct three-dimensional (3-D) refractive index (RI) distributions from highly ill-posed two-dimensional (2-D) measurements using a deep neural network (DNN). Strong distortions are introduced on reconstructions obtained by the Wolf tran ...

2020

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

François Fleuret, Martin Jaggi, Thijs Vogels, Jan Frederik Jonas Florian Mai, Prabhu Teja Sivaprasad

The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under near-optimal problem-specific hyperparameters, and finding these settings may ...

2020

Upgrading the Newsroom: An Automated Image Selection System for News Articles

Karl Aberer, Rémi Philippe Lebret, Fangyu Liu

We propose an automated image selection system to assist photo editors in selecting suitable images for news articles. The system fuses multiple textual sources extracted from news articles and accepts multilingual inputs. It is equipped with char-level wo ...

ASSOC COMPUTING MACHINERY2020

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

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

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

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

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Matthieu Wyart, Mario Geiger, Marco Baity-Jesi, Stefano Spigler, Levent Dogus Sagun

Deep learning has been immensely successful at a variety of tasks, ranging from classification to artificial intelligence. Learning corresponds to fitting training data, which is implemented by descending a very high-dimensional loss function. Understandin ...

AMER PHYSICAL SOC2019

Confidence Matters: Applications to Semantic Segmentation

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Crowding and the Architecture of the Visual System

The Unstoppable Rise of Computational Linguistics in Deep Learning

Three-dimensional tomography of red blood cells using deep learning

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

Upgrading the Newsroom: An Automated Image Selection System for News Articles

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

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

Geometry of adversarial robustness of deep networks: methods and applications

SparseFool: a few pixels make a big difference

Jamming transition as a paradigm to understand the loss landscape of deep neural networks

Three-dimensional tomography of red blood cells using deep learning

Crowding and the Architecture of the Visual System

The Unstoppable Rise of Computational Linguistics in Deep Learning

Upgrading the Newsroom: An Automated Image Selection System for News Articles

Optimizer Benchmarking Needs to Account for Hyperparameter Tuning

Geometry of adversarial robustness of deep networks: methods and applications

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

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

SparseFool: a few pixels make a big difference

Jamming transition as a paradigm to understand the loss landscape of deep neural networks