no code implementations • 12 Dec 2023 • Manuel Röder, Leon Heller, Maximilian Münch, Frank-Michael Schleif
With the advent of interconnected and sensor-equipped edge devices, Federated Learning (FL) has gained significant attention, enabling decentralized learning while maintaining data privacy.
no code implementations • 22 Dec 2022 • Moritz Heusinger, Christoph Raab, Fabrice Rossi, Frank-Michael Schleif
In recent years the applications of machine learning models have increased rapidly, due to the large amount of available data and technological progress. While some domains like web analysis can benefit from this with only minor restrictions, other fields like in medicine with patient data are strongerregulated.
1 code implementation • 18 Dec 2021 • Simon Heilig, Maximilian Münch, Frank-Michael Schleif
Matrix approximations are a key element in large-scale algebraic machine learning approaches.
1 code implementation • 25 Feb 2021 • Christoph Raab, Philipp Väth, Peter Meier, Frank-Michael Schleif
We addressed the first issue by the alignment of transferable spectral properties within an adversarial model to balance the focus between the easily transferable features and the necessary discriminatory features, while at the same time limiting the learning of domain-specific semantics by relevance considerations.
Ranked #10 on Domain Adaptation on ImageCLEF-DA
no code implementations • 31 Aug 2020 • Maximilian Münch, Michiel Straat, Michael Biehl, Frank-Michael Schleif
As an information preserving alternative, we propose a complex-valued vector embedding of proximity data.
no code implementations • 11 Jul 2020 • Christoph Raab, Frank-Michael Schleif
Transfer learning is focused on the reuse of supervised learning models in a new context.
1 code implementation • 10 Jul 2020 • Christoph Raab, Moritz Heusinger, Frank-Michael Schleif
The amount of real-time communication between agents in an information system has increased rapidly since the beginning of the decade.
no code implementations • 25 Sep 2019 • Christoph Raab, Frank-Michael Schleif
The presented approach finds a target subspace representation for source and target data to address domain differences by orthogonal basis transfer.
1 code implementation • 2 Jul 2019 • Christoph Raab, Frank-Michael Schleif
Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification.
no code implementations • 8 Apr 2016 • Frank-Michael Schleif, Andrej Gisbrecht, Peter Tino
Indefinite similarity measures can be frequently found in bio-informatics by means of alignment scores, but are also common in other fields like shape measures in image retrieval.