Search Results for author: Marcel Hildebrandt

Found 12 papers, 7 papers with code

Neuro-symbolic computing with spiking neural networks

1 code implementation4 Aug 2022 Dominik Dold, Josep Soler Garrido, Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler

Knowledge graphs are an expressive and widely used data structure due to their ability to integrate data from different domains in a sensible and machine-readable way.

Graph Embedding Knowledge Graphs +1

On Calibration of Graph Neural Networks for Node Classification

1 code implementation3 Jun 2022 Tong Liu, Yushan Liu, Marcel Hildebrandt, Mitchell Joblin, Hang Li, Volker Tresp

We investigate the calibration of graph neural networks for node classification, study the effect of existing post-processing calibration methods, and analyze the influence of model capacity, graph density, and a new loss function on calibration.

Classification Link Prediction +1

TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs

1 code implementation15 Dec 2021 Yushan Liu, Yunpu Ma, Marcel Hildebrandt, Mitchell Joblin, Volker Tresp

Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types.

Knowledge Graphs Link Prediction

Learning through structure: towards deep neuromorphic knowledge graph embeddings

1 code implementation21 Sep 2021 Victor Caceres Chian, Marcel Hildebrandt, Thomas Runkler, Dominik Dold

Over the recent years, a multitude of different graph neural network architectures demonstrated ground-breaking performances in many learning tasks.

Graph Learning Knowledge Graph Embedding +3

Power to the Relational Inductive Bias: Graph Neural Networks in Electrical Power Grids

no code implementations8 Sep 2021 Martin Ringsquandl, Houssem Sellami, Marcel Hildebrandt, Dagmar Beyer, Sylwia Henselmeyer, Sebastian Weber, Mitchell Joblin

The application of graph neural networks (GNNs) to the domain of electrical power grids has high potential impact on smart grid monitoring.

Inductive Bias

Graphhopper: Multi-Hop Scene Graph Reasoning for Visual Question Answering

1 code implementation13 Jul 2021 Rajat Koner, Hang Li, Marcel Hildebrandt, Deepan Das, Volker Tresp, Stephan Günnemann

We conduct an experimental study on the challenging dataset GQA, based on both manually curated and automatically generated scene graphs.

Navigate Question Answering +1

Scene Graph Reasoning for Visual Question Answering

no code implementations2 Jul 2020 Marcel Hildebrandt, Hang Li, Rajat Koner, Volker Tresp, Stephan Günnemann

We propose a novel method that approaches the task by performing context-driven, sequential reasoning based on the objects and their semantic and spatial relationships present in the scene.

Navigate Question Answering +1

Debate Dynamics for Human-comprehensible Fact-checking on Knowledge Graphs

no code implementations9 Jan 2020 Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

The underlying idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to justify the fact being true (thesis) or the fact being false (antithesis), respectively.

Common Sense Reasoning Fact Checking +3

Reasoning on Knowledge Graphs with Debate Dynamics

2 code implementations2 Jan 2020 Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp

The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments -- paths in the knowledge graph -- with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively.

General Classification Knowledge Graphs +2

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