Publications related to TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding

Related publications (21)

Gradient Approximation of Approximate Multipliers for High-Accuracy Deep Neural Network Retraining

Approximate multipliers (AppMults) are widely employed in deep neural network (DNN) accelerators to reduce the area, delay, and power consumption. However, the inaccuracies of AppMults degrade DNN accuracy, necessitating a retraining process to recover acc ...

Institute of Electrical and Electronics Engineers2025

OPTIMAL TRANSPORT-INSPIRED DEEP LEARNING FRAMEWORK FOR SLOW-DECAYING KOLMOGOROV \bfitn-WIDTH PROBLEMS: EXPLOITING SINKHORN LOSS AND WASSERSTEIN KERNEL

Federico Pichi

Reduced-order models (ROMs) are widely used in scientific computing to tackle high-dimensional systems. However, traditional ROM methods may only partially capture the intrinsic geometric characteristics of the data. These characteristics encompass the und ...

2025

High-dimensional learning of narrow neural networks

Hugo Chao Cui

Recent years have been marked with the fast-pace diversification and increasing ubiquity of machine learning (ML) applications. Yet, a firm theoretical understanding of the surprising efficiency of neural networks (NNs) to learn from high-dimensional data ...

2025

Improving Lipschitz-Constrained Neural Networks by Learning Activation Functions

Michaël Unser, Dimitris Perdios, Alexis Marie Frederic Goujon, Pakshal Narendra Bohra, Sebastian Jonas Neumayer, Stanislas Ducotterd

Lipschitz-constrained neural networks have several advantages over unconstrained ones and can be applied to a variety of problems, making them a topic of attention in the deep learning community. Unfortunately, it has been shown both theoretically and empi ...

2024

Wigner kernels: Body-ordered equivariant machine learning without a basis

Michele Ceriotti, Sergey Pozdnyakov, Filippo Bigi

Machine-learning models based on a point-cloud representation of a physical object are ubiquitous in scientific applications and particularly well-suited to the atomic-scale description of molecules and materials. Among the many different approaches that h ...

AIP Publishing2024

ConGeo: Robust Cross-View Geo-Localization Across Ground View Variations

Devis Tuia, Antoine Bosselut, Javiera Francisca Castillo Navarro, Syrielle Mathilde Marie Montariol, Li Mi

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientat ...

Springer Cham2024

Optimizing Dynamic Aperture Studies with Active Learning

Tatiana Pieloni, Mike Seidel, Davide Di Croce, Ekaterina Krymova

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

Iop Publishing Ltd2024

Comparing transferability in neural network approaches and linear models for machine-learning interaction potentials

Kevin Rossi

Atomic simulations using machine learning interatomic potential (MLIP) have gained a lot of popularity owing to their accuracy in comparison to conventional empirical potentials. However, the transferability of MLIP to systems outside the training set pose ...

AMER PHYSICAL SOC2023

VETIM: Expanding the Vocabulary of Text-to-Image Models only with Text

Sabine Süsstrunk, Radhakrishna Achanta, Mahmut Sami Arpa, Martin Nicolas Everaert

Text-to-image models, such as Stable Diffusion, can generate high-quality images from simple textual prompts. With methods such as Textual Inversion, it is possible to expand the vocabulary of these models with additional concepts, by learning the vocabula ...

BMVA2023

Cardiac output estimated from an uncalibrated radial blood pressure waveform: validation in an in-silico-generated population

Nikolaos Stergiopoulos, Vasiliki Bikia, Georgios Rovas

Background: Cardiac output is essential for patient management in critically ill patients. The state-of-the-art for cardiac output monitoring bears limitations that pertain to the invasive nature of the method, high costs, and associated complications. Hen ...

FRONTIERS MEDIA SA2023

Classification of Tropical Deforestation Drivers with Machine Learning and Satellite Image Time Series

Devis Tuia, Marc Conrad Russwurm, Lloyd Haydn Hughes, Jan Pisl

Tropical deforestation is a major environmental problem with severe consequences such as carbon emissions or biodiversity loss. While much research focuses on monitoring and mapping deforestation, less attention is paid to understanding the various reasons ...

Institute of Electrical and Electronics Engineers2023

Body Part-Based Representation Learning for Occluded Person Re-Identification

Alexandre Massoud Alahi

Occluded person re-identification (ReID) is a person retrieval task which aims at matching occluded person images with holistic ones. For addressing occluded ReID, part-based methods have been shown beneficial as they offer fine-grained information and are ...

IEEE Computer Soc2023

Multicore fiber endoscope for phase imaging based on intensity recording using deep neural networks

Demetri Psaltis, Christophe Moser, Eirini Kakkava, Navid Borhani

The present invention is related to an endoscopic system for phase imaging, comprising a multicore waveguide (080), an optical system comprising at least one first light source (002), for illuminating the sample to be examined, a first camera (010) that is ...

2022

Latent Space Slicing for Enhanced Entropy Modeling in Learning-Based Point Cloud Geometry Compression

Touradj Ebrahimi, Davi Nachtigall Lazzarotto

The growing adoption of point clouds as an imaging modality has stimulated the search for efficient solutions for compression. Learning-based algorithms have been reporting increasingly better performance and are drawing the attention from the research com ...

IEEE2022

A machine learning-driven two-phase metaheuristic for autonomous ridesharing operations

Nikolaos Geroliminis, Claudia Bongiovanni

This paper contributes to the intersection of operations research and machine learning in the context of autonomous ridesharing. In this work, autonomous ridesharing operations are reproduced through an event-based simulation approach and are modeled as a ...

PERGAMON-ELSEVIER SCIENCE LTD2022

Reduced basis methods for time-dependent problems

Jan Sickmann Hesthaven

Numerical simulation of parametrized differential equations is of crucial importance in the study of real-world phenomena in applied science and engineering. Computational methods for real-time and many-query simulation of such problems often require prohi ...

CAMBRIDGE UNIV PRESS2022

Shrinking Bouma's window: How to model crowding in dense displays

Michael Herzog, Gregory Francis, Adrien Christophe Doerig, Alban Bornet

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements ...

2021

Capsule networks, but not convolutional networks explain global configurational visual effects

Michael Herzog, Adrien Christophe Doerig, Alban Bornet

In human vision, perception of local features depends on all elements in the visual field and their exact configuration. For example, observers performed a vernier discrimination task. When a surrounding square was added to the vernier, the task became muc ...

2019

A Product Engine for Energy-Efficient Execution of Binary Neural Networks Using Resistive Memories

David Atienza Alonso, Pierre-Emmanuel Julien Marc Gaillardon, Marina Zapater Sancho, Xifan Tang, Yasir Mahmood Qureshi, Edouard Giacomin

The need for running complex Machine Learning (ML) algorithms, such as Convolutional Neural Networks (CNNs), in edge devices, which are highly constrained in terms of computing power and energy, makes it important to execute such applications efficiently. ...

2019

Central pattern generators for locomotion control in animals and robots: a review

Auke Ijspeert

The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coord ...

2008