no code implementations • 6 Sep 2024 • Malte Luttermann, Ralf Möller, Mattis Hartwig

Probabilistic relational models provide a well-established formalism to combine first-order logic and probabilistic models, thereby allowing to represent relationships between objects in a relational domain.

no code implementations • 18 Jun 2024 • Jinghua Groppe, Sven Groppe, Ralf Möller

Deep learning-based approaches for software vulnerability prediction currently mainly rely on the original text of software code as the feature of nodes in the graph of code and thus could learn a representation that is only specific to the code text, rather than the representation that depicts the 'intrinsic' functionality of a program hidden in the text representation.

1 code implementation • 3 Jun 2024 • Malte Luttermann, Ralf Möller, Marcel Gehrke

Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers.

no code implementations • 6 May 2024 • Malte Luttermann, Edgar Baake, Juljan Bouchagiar, Benjamin Gebel, Philipp Grüning, Dilini Manikwadura, Franziska Schollemann, Elisa Teifke, Philipp Rostalski, Ralf Möller

Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process.

no code implementations • 30 Apr 2024 • Magnus Bender, Tanya Braun, Ralf Möller, Marcel Gehrke

However, the SCD the agent uses might reflect the subjective perspective of another user.

1 code implementation • 15 Mar 2024 • Malte Luttermann, Mattis Hartwig, Tanya Braun, Ralf Möller, Marcel Gehrke

Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.

1 code implementation • 20 Sep 2023 • Malte Luttermann, Tanya Braun, Ralf Möller, Marcel Gehrke

Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes.

no code implementations • 28 Apr 2022 • Ralf Möller

We describe a Lagrange-Newton framework for the derivation of learning rules with desirable convergence properties and apply it to the case of principal component analysis (PCA).

no code implementations • 18 Oct 2021 • Tanya Braun, Stefan Fischer, Florian Lau, Ralf Möller

DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine.

no code implementations • 18 Jul 2020 • Ralf Möller

Fully symmetric learning rules for principal component analysis can be derived from a novel objective function suggested in our previous work.

no code implementations • 24 May 2020 • Ralf Möller

However, for a subspace with multiple axes, the optimization leads to PSA learning rules which only converge to axes spanning the principal subspace but not to the principal eigenvectors.

no code implementations • 25 Mar 2020 • Ralf Möller

A method to derive coupled learning rules from information criteria by Newton optimization is known.

no code implementations • 7 Jan 2020 • Tanya Braun, Ralf Möller

Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them.

no code implementations • 16 Nov 2019 • Marcel Gehrke, Ralf Möller, Tanya Braun

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible.

no code implementations • 2 Jul 2018 • Marcel Gehrke, Tanya Braun, Ralf Möller

The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps.

no code implementations • 2 Jul 2018 • Tanya Braun, Ralf Möller

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowledge compilation (FOKC) based on weighted model counting.

no code implementations • 2 Jul 2018 • Marcel Gehrke, Tanya Braun, Ralf Möller

The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps.

no code implementations • 1 Nov 2016 • Wolfram Schenck, Hendrik Hasenbein, Ralf Möller

This model is based on the internal simulation of movement sequences.

no code implementations • 18 Jul 2016 • Evgeny Kharlamov, Yannis Kotidis, Theofilos Mailis, Christian Neuenstadt, Charalampos Nikolaou, Özgür Özcep, Christoforos Svingos, Dmitriy Zheleznyakov, Sebastian Brandt, Ian Horrocks, Yannis Ioannidis, Steffen Lamparter, Ralf Möller

Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens.

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