Zobrazeno 1 - 10
of 697
pro vyhledávání: '"Xhonneux, A."'
Large language models (LLMs) are vulnerable to adversarial attacks that can bypass their safety guardrails. In many domains, adversarial training has proven to be one of the most promising methods to reliably improve robustness against such attacks.
Externí odkaz:
http://arxiv.org/abs/2405.15589
Current research in adversarial robustness of LLMs focuses on discrete input manipulations in the natural language space, which can be directly transferred to closed-source models. However, this approach neglects the steady progression of open-source
Externí odkaz:
http://arxiv.org/abs/2402.09063
Despite significant investment into safety training, large language models (LLMs) deployed in the real world still suffer from numerous vulnerabilities. One perspective on LLM safety training is that it algorithmically forbids the model from answerin
Externí odkaz:
http://arxiv.org/abs/2402.05723
Publikováno v:
Proceedings of the 18th International IBPSA Conference and Exhibition Building Simulation 2023 (BuildingSimulation 2023)
Regarding climate change, the need to reduce greenhouse gas emissions is well-known. As building heating contributes to a high share of total energy consumption, which relies mainly on fossil energy sources, improving heating efficiency is promising
Externí odkaz:
http://arxiv.org/abs/2311.01800
Taiwan plans to rapidly increase its industrial production capacity of electronic components while concurrently setting policies for its ecological transition. Given that the island is responsible for the manufacturing of a significant part of worldw
Externí odkaz:
http://arxiv.org/abs/2209.12523
Autor:
Xhonneux, Inne1 (AUTHOR) Inne.Xhonneux@uantwerp.be, Marei, Waleed F. A.1,2 (AUTHOR), Meulders, Ben1 (AUTHOR), Slootmans, Jens3,4 (AUTHOR), Pintelon, Isabel5,6 (AUTHOR), Leroy, Jo L. M. R.1 (AUTHOR)
Publikováno v:
PLoS ONE. 6/27/2024, Vol. 19 Issue 6, p1-20. 20p.
Autor:
Zhu, Zhaocheng, Yuan, Xinyu, Galkin, Mikhail, Xhonneux, Sophie, Zhang, Ming, Gazeau, Maxime, Tang, Jian
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalability is limited by the exponential number of paths. Here we present A*N
Externí odkaz:
http://arxiv.org/abs/2206.04798
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-14 (2023)
Abstract Maternal metabolic disorders may cause lipotoxic effects on the developing oocyte. Understanding the timing at which this might disrupt embryo epigenetic programming and how this is linked with mitochondrial dysfunction is crucial for improv
Externí odkaz:
https://doaj.org/article/a3e67ac6e33d4a389c4d1b2936f7a23b
Publikováno v:
Frontiers in Physiology, Vol 15 (2024)
Consumption of obesogenic (OB) diets increases the prevalence of maternal obesity worldwide, causing major psychological and social burdens in women. Obesity not only impacts the mother’s health and fertility but also elevates the risk of obesity a
Externí odkaz:
https://doaj.org/article/3abcc208a63e498b86dac969871fcfe2
Autor:
Zhu, Zhaocheng, Shi, Chence, Zhang, Zuobai, Liu, Shengchao, Xu, Minghao, Yuan, Xinyu, Zhang, Yangtian, Chen, Junkun, Cai, Huiyu, Lu, Jiarui, Ma, Chang, Liu, Runcheng, Xhonneux, Louis-Pascal, Qu, Meng, Tang, Jian
Machine learning has huge potential to revolutionize the field of drug discovery and is attracting increasing attention in recent years. However, lacking domain knowledge (e.g., which tasks to work on), standard benchmarks and data preprocessing pipe
Externí odkaz:
http://arxiv.org/abs/2202.08320