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Found 19 papers, 3 papers with code
Towards Privacy-Preserving Relational Data Synthesis via Probabilistic Relational Models
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.
Variables are a Curse in Software Vulnerability Prediction
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.
Lifting Factor Graphs with Some Unknown Factors
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.
Automated Computation of Therapies Using Failure Mode and Effects Analysis in the Medical Domain
Failure mode and effects analysis (FMEA) is a systematic approach to identify and analyse potential failures and their effects in a system or process.
Enhancement of Subjective Content Descriptions by using Human Feedback
However, the SCD the agent uses might reflect the subjective perspective of another user.
Lifted Causal Inference in Relational Domains
Lifted inference exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, thereby speeding up query answering while maintaining exact answers.
Colour Passing Revisited: Lifted Model Construction with Commutative Factors
Lifted probabilistic inference exploits symmetries in a probabilistic model to allow for tractable probabilistic inference with respect to domain sizes.
Derivation of Learning Rules for Coupled Principal Component Analysis in a Lagrange-Newton Framework
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).
Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress
DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine.
Improved Convergence Speed of Fully Symmetric Learning Rules for Principal Component Analysis
Fully symmetric learning rules for principal component analysis can be derived from a novel objective function suggested in our previous work.
Derivation of Symmetric PCA Learning Rules from a Novel Objective Function
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.
Derivation of Coupled PCA and SVD Learning Rules from a Newton Zero-Finding Framework
A method to derive coupled learning rules from information criteria by Newton optimization is known.
Exploring Unknown Universes in Probabilistic Relational Models
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them.
Taming Reasoning in Temporal Probabilistic Relational Models
Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible.
Answering Hindsight Queries with Lifted Dynamic Junction Trees
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.
Fusing First-order Knowledge Compilation and the Lifted Junction Tree Algorithm
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.
Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
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.
Detecting Affordances by Visuomotor Simulation
This model is based on the internal simulation of movement sequences.
Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)
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.
