1 code implementation • 18 Nov 2022 • Thomas Altstidl, An Nguyen, Leo Schwinn, Franz Köferl, Christopher Mutschler, Björn Eskofier, Dario Zanca
We also demonstrate that our family of models is able to generalize well towards larger scales and improve scale equivariance.
no code implementations • 1 Sep 2022 • Shakir Laher, Carla Brackstone, Sara Reis, An Nguyen, Sean White, Ibrahim Habli
In recent years, the number of machine learning (ML) technologies gaining regulatory approval for healthcare has increased significantly allowing them to be placed on the market.
no code implementations • 19 May 2022 • Leo Schwinn, Leon Bungert, An Nguyen, René Raab, Falk Pulsmeyer, Doina Precup, Björn Eskofier, Dario Zanca
The reliability of neural networks is essential for their use in safety-critical applications.
no code implementations • 4 May 2022 • Sami Ede, Serop Baghdadlian, Leander Weber, An Nguyen, Dario Zanca, Wojciech Samek, Sebastian Lapuschkin
The ability to continuously process and retain new information like we do naturally as humans is a feat that is highly sought after when training neural networks.
no code implementations • 8 Aug 2021 • An Nguyen
Although current state-of-the-art language models have achieved impressive results in numerous natural language processing tasks, still they could not solve the problem of producing repetitive, dull and sometimes inconsistent text in open-ended text generation.
no code implementations • 21 May 2021 • Leo Schwinn, René Raab, An Nguyen, Dario Zanca, Bjoern Eskofier
Progress in making neural networks more robust against adversarial attacks is mostly marginal, despite the great efforts of the research community.
1 code implementation • 24 Feb 2021 • Leo Schwinn, An Nguyen, René Raab, Leon Bungert, Daniel Tenbrinck, Dario Zanca, Martin Burger, Bjoern Eskofier
The susceptibility of deep neural networks to untrustworthy predictions, including out-of-distribution (OOD) data and adversarial examples, still prevent their widespread use in safety-critical applications.
1 code implementation • 11 Jan 2021 • An Nguyen, Stefan Foerstel, Thomas Kittler, Andrey Kurzyukov, Leo Schwinn, Dario Zanca, Tobias Hipp, Da Jun Sun, Michael Schrapp, Eva Rothgang, Bjoern Eskofier
The overall framework is currently deployed, learns and evaluates predictive models from terabytes of IoT and enterprise data to actively monitor the customer sentiment for a fleet of thousands of high-end medical devices.
no code implementations • 5 Nov 2020 • Leo Schwinn, An Nguyen, René Raab, Dario Zanca, Bjoern Eskofier, Daniel Tenbrinck, Martin Burger
We empirically show that by incorporating this nonlocal gradient information, we are able to give a more accurate estimation of the global descent direction on noisy and non-convex loss surfaces.
1 code implementation • 21 Oct 2020 • An Nguyen, Wenyu Zhang, Leo Schwinn, Bjoern Eskofier
Process Mining has recently gained popularity in healthcare due to its potential to provide a transparent, objective and data-based view on processes.
1 code implementation • 2 Oct 2020 • An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern Eskofier
To better model the time dependencies between events, we propose a new PBPM technique based on time-aware LSTM (T-LSTM) cells.
no code implementations • EACL 2017 • Vasanthi Vuppuluri, Shahryar Baki, An Nguyen, Rakesh Verma
Collocation and idiom extraction are well-known challenges with many potential applications in Natural Language Processing (NLP).