Search Results for author: Dominik Dold

Found 15 papers, 7 papers with code

Towards Large-scale Network Emulation on Analog Neuromorphic Hardware

no code implementations30 Jan 2024 Elias Arnold, Philipp Spilger, Jan V. Straub, Eric Müller, Dominik Dold, Gabriele Meoni, Johannes Schemmel

We demonstrate the training of two deep spiking neural network models, using the MNIST and EuroSAT datasets, that exceed the physical size constraints of a single-chip BrainScaleS-2 system.

Differentiable graph-structured models for inverse design of lattice materials

1 code implementation11 Apr 2023 Dominik Dold, Derek Aranguren van Egmond

Architected materials possessing physico-chemical properties adaptable to disparate environmental conditions embody a disruptive new domain of materials science.

Graph Neural Network

Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods

no code implementations23 Dec 2022 Anna Himmelhuber, Dominik Dold, Stephan Grimm, Sonja Zillner, Thomas Runkler

Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain.

Decision Making Explainable Artificial Intelligence (XAI) +3

Selected Trends in Artificial Intelligence for Space Applications

no code implementations10 Dec 2022 Dario Izzo, Gabriele Meoni, Pablo Gómez, Dominik Dold, Alexander Zoechbauer

The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced.

Neuromorphic Computing and Sensing in Space

no code implementations10 Dec 2022 Dario Izzo, Alexander Hadjiivanov, Dominik Dold, Gabriele Meoni, Emmanuel Blazquez

The term ``neuromorphic'' refers to systems that are closely resembling the architecture and/or the dynamics of biological neural networks.

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 Graph Neural Network +2

Relational representation learning with spike trains

no code implementations18 May 2022 Dominik Dold

Relational representation learning has lately received an increase in interest due to its flexibility in modeling a variety of systems like interacting particles, materials and industrial projects for, e. g., the design of spacecraft.

Knowledge Graph Embedding Knowledge Graphs +1

An energy-based model for neuro-symbolic reasoning on knowledge graphs

1 code implementation4 Oct 2021 Dominik Dold, Josep Soler Garrido

Machine learning on graph-structured data has recently become a major topic in industry and research, finding many exciting applications such as recommender systems and automated theorem proving.

Automated Theorem Proving Graph Embedding +2

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 Graph Neural Network +4

Machine learning on knowledge graphs for context-aware security monitoring

1 code implementation18 May 2021 Josep Soler Garrido, Dominik Dold, Johannes Frank

Machine learning techniques are gaining attention in the context of intrusion detection due to the increasing amounts of data generated by monitoring tools, as well as the sophistication displayed by attackers in hiding their activity.

BIG-bench Machine Learning Intrusion Detection +2

SpikE: spike-based embeddings for multi-relational graph data

1 code implementation27 Apr 2021 Dominik Dold, Josep Soler Garrido

We propose a spike-based algorithm where nodes in a graph are represented by single spike times of neuron populations and relations as spike time differences between populations.

Graph Embedding Knowledge Graphs

Stochasticity from function -- why the Bayesian brain may need no noise

no code implementations21 Sep 2018 Dominik Dold, Ilja Bytschok, Akos F. Kungl, Andreas Baumbach, Oliver Breitwieser, Walter Senn, Johannes Schemmel, Karlheinz Meier, Mihai A. Petrovici

An increasing body of evidence suggests that the trial-to-trial variability of spiking activity in the brain is not mere noise, but rather the reflection of a sampling-based encoding scheme for probabilistic computing.

Bayesian Inference

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