23 citations · 28 across the 10 of their papers we have counts for
10 papers
A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models
Sahar Sadrizadeh, Clément Barbier, Ljiljana Dolamic +1
In this paper, we propose an optimization-based adversarial attack against Neural Machine Translation (NMT) models. First, we propose an optimization problem to generate adversaria…
Evolutionary Algorithms in the Light of SGD: Limit Equivalence, Minima Flatness, and Transfer Learning
Andrei Kucharavy, Rachid Guerraoui, Ljiljana Dolamic
Whenever applicable, the Stochastic Gradient Descent (SGD) has shown itself to be unreasonably effective. Instead of underperforming and getting trapped in local minima due to the…
Byzantine-Resilient Learning Beyond Gradients: Distributing Evolutionary Search
Andrei Kucharavy, Matteo Monti, Rachid Guerraoui +1
Modern machine learning (ML) models are capable of impressive performances. However, their prowess is not due only to the improvements in their architecture and training algorithms…
Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense
Andrei Kucharavy, Zachary Schillaci, Loïc Maréchal +6
Generative Language Models gained significant attention in late 2022 / early 2023, notably with the introduction of models refined to act consistently with users' expectations of i…
Targeted Adversarial Attacks against Neural Machine Translation
Sahar Sadrizadeh, AmirHossein Dabiri Aghdam, Ljiljana Dolamic +1
Neural Machine Translation (NMT) systems are used in various applications. However, it has been shown that they are vulnerable to very small perturbations of their inputs, known as…
TransFool: An Adversarial Attack against Neural Machine Translation Models
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Deep neural networks have been shown to be vulnerable to small perturbations of their inputs, known as adversarial attacks. In this paper, we investigate the vulnerability of Neura…
Identifying Emerging Technologies and Leading Companies using Network Dynamics of Patent Clusters: a Cybersecurity Case Study
Michael Tsesmelis, Ljiljana Dolamic, Marcus Matthias Keupp +2
Strategic decisions rely heavily on non-scientific instrumentation to forecast emerging technologies and leading companies. Instead, we build a fast quantitative system with a smal…
Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale
Cyril Vallez, Andrei Kucharavy, Ljiljana Dolamic
The advent of the internet, followed shortly by the social media made it ubiquitous in consuming and sharing information between anyone with access to it. The evolution in the cons…
Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers
Sahar Sadrizadeh, Ljiljana Dolamic, Pascal Frossard
Recently, it has been shown that, in spite of the significant performance of deep neural networks in different fields, those are vulnerable to adversarial examples. In this paper,…
From Scattered Sources to Comprehensive Technology Landscape: A Recommendation-based Retrieval Approach
Chi Thang Duong, Dimitri Percia David, Ljiljana Dolamic +3
Mapping the technology landscape is crucial for market actors to take informed investment decisions. However, given the large amount of data on the Web and its subsequent informati…