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of 83
pro vyhledávání: '"Baer, Andreas"'
Training a linear classifier or lightweight model on top of pretrained vision model outputs, so-called 'frozen features', leads to impressive performance on a number of downstream few-shot tasks. Currently, frozen features are not modified during tra
Externí odkaz:
http://arxiv.org/abs/2403.10519
Autor:
Höllring, Kevin, Baer, Andreas, Vučemilović-Alagić, Nataša, Smith, David M., Smith, Ana-Sunčana
Hypothesis:Diffusion in confinement is an important fundamental problem with significant implications for applications of supported liquid phases. However, resolving the spatially dependent diffusion coefficient, parallel and perpendicular to interfa
Externí odkaz:
http://arxiv.org/abs/2306.15094
Autor:
Baer, Andreas, Wawra, Simon E., Bielmeier, Kristina, Uttinger, Maximilian J., Smith, David M., Peukert, Wolfgang, Walter, Johannes, Smith, Ana-Sunčana
The Stokes-Einstein-Sutherland (SES) equation is at the foundation of statistical physics, relating a particle's diffusion coefficient and size with the fluid viscosity, temperature and the boundary condition for the particle-solvent interface. It is
Externí odkaz:
http://arxiv.org/abs/2302.07359
Autor:
Höllring, Kevin, Baer, Andreas, Vučemilović-Alagić, Nataša, Smith, David M., Smith, Ana-Sunčana
Hypothesis: Diffusion in confinement is an important fundamental problem with significant implications for applications of supported liquid phases. However, resolving the spatially dependent diffusion coefficient, parallel and perpendicular to interf
Externí odkaz:
http://arxiv.org/abs/2212.09545
Accurate modeling of diffusive transport of nanoparticles across nanopores is a particularly challenging problem. The reason is that for such narrow pores the large surface-to-volume ratio amplifies the relevance of the nanoscopic details and of the
Externí odkaz:
http://arxiv.org/abs/2211.07414
While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial perturbations limits their use in practical applications. In this paper, we (i) propose a novel adversarial perturbatio
Externí odkaz:
http://arxiv.org/abs/2203.01177
Autor:
Höllring, Kevin, Baer, Andreas, Vučemilović-Alagić, Nataša, Smith, David M., Smith, Ana-Sunčana
Publikováno v:
In Journal of Colloid And Interface Science March 2024 657:272-289
Autor:
Houben, Sebastian, Abrecht, Stephanie, Akila, Maram, Bär, Andreas, Brockherde, Felix, Feifel, Patrick, Fingscheidt, Tim, Gannamaneni, Sujan Sai, Ghobadi, Seyed Eghbal, Hammam, Ahmed, Haselhoff, Anselm, Hauser, Felix, Heinzemann, Christian, Hoffmann, Marco, Kapoor, Nikhil, Kappel, Falk, Klingner, Marvin, Kronenberger, Jan, Küppers, Fabian, Löhdefink, Jonas, Mlynarski, Michael, Mock, Michael, Mualla, Firas, Pavlitskaya, Svetlana, Poretschkin, Maximilian, Pohl, Alexander, Ravi-Kumar, Varun, Rosenzweig, Julia, Rottmann, Matthias, Rüping, Stefan, Sämann, Timo, Schneider, Jan David, Schulz, Elena, Schwalbe, Gesina, Sicking, Joachim, Srivastava, Toshika, Varghese, Serin, Weber, Michael, Wirkert, Sebastian, Wirtz, Tim, Woehrle, Matthias
Publikováno v:
Fingscheidt, T., Gottschalk, H., Houben, S. (eds) Deep Neural Networks and Data for Automated Driving, Springer, Cham (2022)
The use of deep neural networks (DNNs) in safety-critical applications like mobile health and autonomous driving is challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over i
Externí odkaz:
http://arxiv.org/abs/2104.14235
In this work we address the task of observing the performance of a semantic segmentation deep neural network (DNN) during online operation, i.e., during inference, which is of high importance in safety-critical applications such as autonomous driving
Externí odkaz:
http://arxiv.org/abs/2104.05255