Zobrazeno 1 - 10
of 957
pro vyhledávání: '"Assion, A."'
Autor:
Assion, Felix, Gressner, Florens, Augustine, Nitin, Klemenc, Jona, Hammam, Ahmed, Krattinger, Alexandre, Trittenbach, Holger, Riemer, Sascha
High-autonomy vehicle functions rely on machine learning (ML) algorithms to understand the environment. Despite displaying remarkable performance in fair weather scenarios, perception algorithms are heavily affected by adverse weather and lighting co
Externí odkaz:
http://arxiv.org/abs/2408.06071
Autor:
Bartoš, František, Sarafoglou, Alexandra, Godmann, Henrik R., Sahrani, Amir, Leunk, David Klein, Gui, Pierre Y., Voss, David, Ullah, Kaleem, Zoubek, Malte J., Nippold, Franziska, Aust, Frederik, Vieira, Felipe F., Islam, Chris-Gabriel, Zoubek, Anton J., Shabani, Sara, Petter, Jonas, Roos, Ingeborg B., Finnemann, Adam, Lob, Aaron B., Hoffstadt, Madlen F., Nak, Jason, de Ron, Jill, Derks, Koen, Huth, Karoline, Terpstra, Sjoerd, Bastelica, Thomas, Matetovici, Magda, Ott, Vincent L., Zetea, Andreea S., Karnbach, Katharina, Donzallaz, Michelle C., John, Arne, Moore, Roy M., Assion, Franziska, van Bork, Riet, Leidinger, Theresa E., Zhao, Xiaochang, Motaghi, Adrian Karami, Pan, Ting, Armstrong, Hannah, Peng, Tianqi, Bialas, Mara, Pang, Joyce Y. -C., Fu, Bohan, Yang, Shujun, Lin, Xiaoyi, Sleiffer, Dana, Bognar, Miklos, Aczel, Balazs, Wagenmakers, Eric-Jan
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected $350{,}757$ coin flips to test the counterintuitive prediction from a physics model of human
Externí odkaz:
http://arxiv.org/abs/2310.04153
Machine learning image classifiers are susceptible to adversarial and corruption perturbations. Adding imperceptible noise to images can lead to severe misclassifications of the machine learning model. Using $L_p$-norms for measuring the size of the
Externí odkaz:
http://arxiv.org/abs/2110.06816
Autor:
Schwerdtner, Paul, Greßner, Florens, Kapoor, Nikhil, Assion, Felix, Sass, René, Günther, Wiebke, Hüger, Fabian, Schlicht, Peter
In this paper we propose a framework for assessing the risk associated with deploying a machine learning model in a specified environment. For that we carry over the risk definition from decision theory to machine learning. We develop and implement a
Externí odkaz:
http://arxiv.org/abs/2011.04328
Despite achieving remarkable performance on many image classification tasks, state-of-the-art machine learning (ML) classifiers remain vulnerable to small input perturbations. Especially, the existence of adversarial examples raises concerns about th
Externí odkaz:
http://arxiv.org/abs/2002.01810
Autor:
Meteyake, Hèzouwè T. *, Collin, Anne, Bilalissi, Abidi *, Dassidi, Nideou, Assion, Mauril E.P. *, Tona, Kokou *
Publikováno v:
In Poultry Science October 2023 102(10)
Autor:
Hèzouwè T. Meteyake, Anne Collin, Abidi Bilalissi, Nideou Dassidi, Mauril E.P. Assion, Kokou Tona
Publikováno v:
Poultry Science, Vol 102, Iss 10, Pp 102912- (2023)
ABSTRACT: Many studies have shown that thermal manipulations during the incubation (TMI) and naked neck gene (Na) positively affect heat-stressed broilers' thermotolerance, hatching process, and posthatch performance. Their combination could increase
Externí odkaz:
https://doaj.org/article/f166a74f2ea54873ae47e6874f2511b1
Autor:
Assion, Felix, Schlicht, Peter, Greßner, Florens, Günther, Wiebke, Hüger, Fabian, Schmidt, Nico, Rasheed, Umair
Most state-of-the-art machine learning (ML) classification systems are vulnerable to adversarial perturbations. As a consequence, adversarial robustness poses a significant challenge for the deployment of ML-based systems in safety- and security-crit
Externí odkaz:
http://arxiv.org/abs/1906.07077
Publikováno v:
Opt. Lett. 44, 1464-1467 (2019)
We demonstrate a single-stage, multipass Ti:sapphire amplifier capable of delivering sub-13 fs, 3.2 mJ pulses at 1 kHz repetition rate. Gaussian filters are used to suppress the gain-narrowing effect, thereby enabling the achievement of an ultrabroad
Externí odkaz:
http://arxiv.org/abs/1905.02707
Publikováno v:
In Zeitschrift fuer Evidenz, Fortbildung und Qualitaet im Gesundheitswesen May 2023 178:22-28