Search Results for author: Frank-Michael Schleif

Found 9 papers, 4 papers with code

Federated Learning -- Methods, Applications and beyond

no code implementations22 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.

Federated Learning Transfer Learning

Revisiting Memory Efficient Kernel Approximation: An Indefinite Learning Perspective

1 code implementation18 Dec 2021 Simon Heilig, Maximilian Münch, Frank-Michael Schleif

Matrix approximations are a key element in large-scale algebraic machine learning approaches.

Bridging Adversarial and Statistical Domain Transfer via Spectral Adaptation Networks

1 code implementation25 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.

Domain Adaptation

Reactive Soft Prototype Computing for Concept Drift Streams

1 code implementation10 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.

Domain Adaptation via Low-Rank Basis Approximation

no code implementations25 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.

Domain Adaptation

Low-Rank Subspace Override for Unsupervised Domain Adaptation

1 code implementation2 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.

General Classification Image Classification +1

Probabilistic classifiers with low rank indefinite kernels

no code implementations8 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.

Image Retrieval Retrieval

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