Publications related to A geometry-inspired decision-based attack

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Pascal Frossard, Seyed Mohsen Moosavi Dezfooli, Michail Vlachos, Ahmad Ajalloeian

We investigate methods for manipulating classifier explanations while keeping the predictions unchanged. Our focus is on using a sparse attack, which seeks to alter only a minimal number of input features. We present an efficient and novel algorithm for co ...

Los Alamitos2023

Predicting nonlinear optical scattering with physics-driven neural networks

Demetri Psaltis, Carlo Gigli, Ahmed Ayoub

Deep neural networks trained on physical losses are emerging as promising surrogates for nonlinear numerical solvers. These tools can predict solutions to Maxwell's equations and compute gradients of output fields with respect to the material and geometric ...

AIP Publishing2023

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Daniele Martinelli

In this work, we introduce and study a class of Deep Neural Networks (DNNs) in continuous-time. The proposed architecture stems from the combination of Neural Ordinary Differential Equations (Neural ODEs) with the model structure of recently introduced Rec ...

New York2023

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

David Atienza Alonso, Amir Aminifar, Tomas Teijeiro Campo, Alireza Amirshahi, Saleh Baghersalimi

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

2023

Exploring neural networks and their potential for species distribution modeling

Devis Tuia, Benjamin Alexander Kellenberger, Nina Marion Aurélia Van Tiel, Robin Adrien Zbinden, Lloyd Haydn Hughes

Species distribution models (SDMs) relate species occurrence data with environmental variables and are used to understand and predict species distributions across landscapes. While some machine learning models have been adopted by the SDM community, recent ...

2023

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Zhixiong Jin

We propose a novel system leveraging deep learning-based methods to predict urban traffic accidents and estimate their severity. The major challenge is the data imbalance problem in traffic accident prediction. The problem is caused by numerous zero values ...

Basel2023

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Pascal Frossard, Ádám Dániel Ivánkay

Along with the successful deployment of deep neural networks in several application domains, the need to unravel the black-box nature of these networks has seen a significant increase recently. Several methods have been introduced to provide insight into t ...

Assoc Computational Linguistics-Acl2023

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Ahmet Caner Yüzügüler

Deep neural networks (DNN) have become an essential tool to tackle challenging tasks in many fields of computer science. However, their high computational complexity limits their applicability. Specialized DNN accelerators have been developed to accommodat ...

EPFL2023

Improved calorimetric particle identification in NA62 using machine learning techniques

Alessandro Mapelli, Radoslav Marchevski, Alina Kleimenova

Measurement of the ultra-rare K+ -> pi(+)nu(nu) over bar over bar decay at the NA62 experiment at CERN requires high-performance particle identification to distinguish muons from pions. Calorimetric identification currently in use, based on a boosted decis ...

New York2023

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sabine Süsstrunk, Mathieu Salzmann, Yulun Jiang, Chen Liu, Zhuoyi Huang

We address the problem of stably and efficiently training a deep neural network robust to adversarial perturbations bounded by an

l_1

norm. We demonstrate that achieving robustness against

l_1

-bounded perturbations is more challenging than in the

l_2

...

2023

A geometry-inspired decision-based attack

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Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Predicting nonlinear optical scattering with physics-driven neural networks

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Exploring neural networks and their potential for species distribution modeling

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Improved calorimetric particle identification in NA62 using machine learning techniques

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications

Towards Stable and Efficient Adversarial Training against $l_1$ Bounded Adversarial Attacks

Sparse Attacks for Manipulating Explanations in Deep Neural Network Models

Predicting nonlinear optical scattering with physics-driven neural networks

Improved calorimetric particle identification in NA62 using machine learning techniques

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations

From Prediction to Prevention: Leveraging Deep Learning in Traffic Accident Prediction Systems

Exploring neural networks and their potential for species distribution modeling