Concept

Réseau neuronal résiduel

Publications associées (534)

On Certifying Non-Uniform Bounds against Adversarial Attacks

This work studies the robustness certification problem of neural network models, which aims to find certified adversary-free regions as large as possible around data points. In contrast to the existing approaches that seek regions bounded uniformly along a ...

2019

Estimation of groundwater storage from seismic data using deep learning

Jan Sickmann Hesthaven

We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks a ...

Wiley2019

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

Beauty and the Beast: Optimal Methods Meet Learning for Drone Racing

Davide Scaramuzza

Autonomous micro aerial vehicles still struggle with fast and agile maneuvers, dynamic environments, imperfect sensing, and state estimation drift. Autonomous drone racing brings these challenges to the fore. Human pilots can fly a previously unseen track ...

2019

Probabilistic Atlases to Enforce Topological Constraints

Pascal Fua, Graham Knott, Pamuditha Udaranga Wickramasinghe

Probabilistic atlases (PAs) have long been used in standard segmentation approaches and, more recently, in conjunction with Convolutional Neural Networks (CNNs). However, their use has been restricted to relatively standardized structures such as the brain ...

2019

DermoNet: densely linked convolutional neural network for efficient skin lesion segmentation

Jean-Philippe Thiran, Hazim Kemal Ekenel, Saleh Baghersalimi

Recent state-of-the-art methods for skin lesion segmentation are based on convolutional neural networks (CNNs). Even though these CNN-based segmentation approaches are accurate, they are computationally expensive. In this paper, we address this problem and ...

2019

An improved objective metric to predict image quality using deep neural networks

Touradj Ebrahimi, Pinar Akyazi

Objective quality assessment of compressed images is very useful in many applications. In this paper we present an objective quality metric that is better tuned to evaluate the quality of images distorted by compression artifacts. A deep convolutional neur ...

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

Deep learning assisted image transmission in multimode fibers

Demetri Psaltis, Christophe Moser, Damien Claude-Marie Loterie, Babak Rahmani, Georgia Konstantinou

We propose a data -driven approach for light transmission control inside multimode fibers (MMFs). Specifically, we show that a convolutional neural network is able to reconstruct amplitude/phase modulated images from scrambled amplitude -only images obtain ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Multilingual Bottleneck Features For Query By Example Spoken Term Detection

Hervé Bourlard, Dhananjay Ram

State of the art solutions to query by example spoken term detection (QbE-STD) rely on bottleneck feature representation of the query and audio document. Here, we present a study on QbE-STD performance using several monolingual as well as multilingual bott ...

IEEE2019

Graph Chatbot

Chattez avec Graph Search

Posez n’importe quelle question sur les cours, conférences, exercices, recherches, actualités, etc. de l’EPFL ou essayez les exemples de questions ci-dessous.

AVERTISSEMENT : Le chatbot Graph n'est pas programmé pour fournir des réponses explicites ou catégoriques à vos questions. Il transforme plutôt vos questions en demandes API qui sont distribuées aux différents services informatiques officiellement administrés par l'EPFL. Son but est uniquement de collecter et de recommander des références pertinentes à des contenus que vous pouvez explorer pour vous aider à répondre à vos questions.

Connectez-vous pour utiliser Chat avec Graph Search