2 code implementations • 28 Sep 2023 • Maria-Irina Nicolae, Max Eisele, Andreas Zeller
In this paper, we conduct the most extensive evaluation of NPS fuzzers against standard gray-box fuzzers (>11 CPU years and >5. 5 GPU years), and make the following contributions: (1) We find that the original performance claims for NPS fuzzers do not hold; a gap we relate to fundamental, implementation, and experimental limitations of prior works.
5 code implementations • 3 Jul 2018 • Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran, Beat Buesser, Ambrish Rawat, Martin Wistuba, Valentina Zantedeschi, Nathalie Baracaldo, Bryant Chen, Heiko Ludwig, Ian M. Molloy, Ben Edwards
Defending Machine Learning models involves certifying and verifying model robustness and model hardening with approaches such as pre-processing inputs, augmenting training data with adversarial samples, and leveraging runtime detection methods to flag any inputs that might have been modified by an adversary.
no code implementations • 22 Nov 2017 • Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
Deep Learning models are vulnerable to adversarial examples, i. e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high confidence.
no code implementations • 28 Aug 2017 • Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae
First, a knowledge-graph representation is learned to embed a large set of entities into a semantic space.
no code implementations • 21 Jul 2017 • Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.
no code implementations • 15 Oct 2016 • Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard, Marc Sebban
In this paper, we propose a novel method for learning similarities based on DTW, in order to improve time series classification.
no code implementations • 19 Dec 2014 • Maria-Irina Nicolae, Marc Sebban, Amaury Habrard, Éric Gaussier, Massih-Reza Amini
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking.