Zobrazeno 1 - 10
of 33 527
pro vyhledávání: '"A Fort"'
In November 2023, the UK and US announced the creation of their AI Safety Institutes (AISIs). Five other jurisdictions have followed in establishing AISIs or similar institutions, with more likely to follow. While there is considerable variation betw
Externí odkaz:
http://arxiv.org/abs/2410.09219
We report on the formation of multiple quantum droplets in a heteronuclear $^{41}$K-$^{87}$Rb mixture released in an optical waveguide. By a sudden change of the interspecies interaction from the non-interacting to the strongly attractive regime, we
Externí odkaz:
http://arxiv.org/abs/2409.16017
Autor:
Fort, Kristina
International standards are crucial for ensuring that frontier AI systems are developed and deployed safely around the world. Since the AI Safety Institutes (AISIs) possess in-house technical expertise, mandate for international engagement, and conve
Externí odkaz:
http://arxiv.org/abs/2409.11314
Adversarial examples pose a significant challenge to the robustness, reliability and alignment of deep neural networks. We propose a novel, easy-to-use approach to achieving high-quality representations that lead to adversarial robustness through the
Externí odkaz:
http://arxiv.org/abs/2408.05446
Autor:
Fort, Stanislav
We explore a class of adversarial attacks targeting the activations of language models. By manipulating a relatively small subset of model activations, $a$, we demonstrate the ability to control the exact prediction of a significant number (in some c
Externí odkaz:
http://arxiv.org/abs/2312.02780
Very distinct strategies can be deployed to recognize and characterize an unknown environment or a shape. A recent and promising approach, especially in robotics, is to reduce the complexity of the exploratory units to a minimum. Here, we show that t
Externí odkaz:
http://arxiv.org/abs/2308.04848
Autor:
Fort, Stanislav
We show that we can easily design a single adversarial perturbation $P$ that changes the class of $n$ images $X_1,X_2,\dots,X_n$ from their original, unperturbed classes $c_1, c_2,\dots,c_n$ to desired (not necessarily all the same) classes $c^*_1,c^
Externí odkaz:
http://arxiv.org/abs/2308.03792
The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research
Autor:
Abdalla, Mohamed, Wahle, Jan Philip, Ruas, Terry, Névéol, Aurélie, Ducel, Fanny, Mohammad, Saif M., Fort, Karën
Publikováno v:
ACL 2023
Recent advances in deep learning methods for natural language processing (NLP) have created new business opportunities and made NLP research critical for industry development. As one of the big players in the field of NLP, together with governments a
Externí odkaz:
http://arxiv.org/abs/2305.02797
Autor:
Hernandez-Rajkov, D., Grani, N., Scazza, F., Del Pace, G., Kwon, W. J., Inguscio, M., Xhani, K., Fort, C., Modugno, M., Marino, F., Roati, G.
Publikováno v:
Nature Physics (2024)
At the interface between two fluid layers in relative motion, infinitesimal fluctuations can be exponentially amplified, inducing vorticity and the breakdown of the laminar flow. This process, known as the Kelvin-Helmholtz instability, is responsible
Externí odkaz:
http://arxiv.org/abs/2303.12631
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity to deal with a large amount of data observed with uncertainties. An ex
Externí odkaz:
http://arxiv.org/abs/2302.11147