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Metadata Source: DaRUS

401 to 410 of 2,699 Results

Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Nayyeri, Mojtaba; Cochez, Michael; Staab, Steffen, 2024, "Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction", https://doi.org/10.18419/DARUS-3988, DaRUS, V1 This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For mor...
Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24) Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Nayyeri, Mojtaba; Luo, Linhao; Wang, Zihao; Pan, Shirui; Staab, Steffen, 2024, "Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)", https://doi.org/10.18419/DARUS-3978, DaRUS, V1 This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, Preside...
Code for Ultrahyperbolic Knowledge Graph Embeddings Jul 5, 2024 - KnowGraphs (EU) Xiong, Bo; Zhu, Shichao; Nayyeri, Mojtaba; Xu, Chengjin; Pan, Shirui; Staab, Steffen, 2024, "Code for Ultrahyperbolic Knowledge Graph Embeddings", https://doi.org/10.18419/DARUS-4342, DaRUS, V1 This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the...
Code for Pseudo-Riemannian Graph Convolutional Networks Jul 5, 2024 - Analytic Computing Xiong, Bo; Zhu, Shichao; Potyka, Nico; Pan, Shirui; Zhou, Chuan; Staab, Steffen, 2024, "Code for Pseudo-Riemannian Graph Convolutional Networks", https://doi.org/10.18419/DARUS-4340, DaRUS, V1, UNF:6:XC5GdaJdFoY7V7SNqvdoiQ== [fileUNF] This dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation. This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or...
KnowGraphs (EU)(Universität Stuttgart) Jul 3, 2024Analytic Computing Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/knowngraphs/
Hard Negative Captions(Universität Stuttgart) Jul 3, 2024PN 6-5 (II)
PN 6-5 (II)(Universität Stuttgart) Jul 3, 2024PN 6 SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"
Code for training and using the droplet segmentation models Jul 2, 2024 - Spray Segmentation Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the droplet segmentation models", https://doi.org/10.18419/DARUS-4147, DaRUS, V1 This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md.
Dissertation Reiff(Universität Stuttgart) Jul 1, 2024ISW Data Repository
Replication Data for: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers - Two Superfacility Case Studies Jun 28, 2024 - Scientific Computing Pollinger, Theresa; Van Craen, Alexander; Offenhäuser, Philipp, 2024, "Replication Data for: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers - Two Superfacility Case Studies", https://doi.org/10.18419/DARUS-3707, DaRUS, V1 This data repository contains input and output files for large-scale experiments conducted on the three German national supercomputers HAWK, SuperMUC-NG, and JUWELS. More structure can be seen when switching to "Tree" view below. Then, the files are structured into three folders (`io-benchmark`, `two-systems, `three-systems`), and the outputs were...

Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Nayyeri, Mojtaba; Cochez, Michael; Staab, Steffen, 2024, "Code for Hyperbolic Embedding Inference for Structured Multi-Label Prediction", https://doi.org/10.18419/DARUS-3988, DaRUS, V1

This is a PyTorch implementation of the paper Hyperbolic Embedding Inference for Structured Multi-Label Prediction published in NeurIPS 2022. The code provides the Python scripts to reproduce the experiments in the paper, as well as a proof-of-concept example of the method. To execute the code, follow the instructions in the README.md file. For mor...

Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Nayyeri, Mojtaba; Luo, Linhao; Wang, Zihao; Pan, Shirui; Staab, Steffen, 2024, "Replication Data for NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)", https://doi.org/10.18419/DARUS-3978, DaRUS, V1

This code is a PyTorch implementation of the paper "NestE: Modeling Nested Relational Structures for Knowledge Graph Reasoning (AAAI'24)". NestE is a knowledge graph embedding method that can encode nested facts represented by quoted triples (h,r,t) in which the subject and object are triples themselves, e.g., ((BarackObama, holds_position, Preside...

Code for Ultrahyperbolic Knowledge Graph Embeddings

Jul 5, 2024 - KnowGraphs (EU)

Xiong, Bo; Zhu, Shichao; Nayyeri, Mojtaba; Xu, Chengjin; Pan, Shirui; Staab, Steffen, 2024, "Code for Ultrahyperbolic Knowledge Graph Embeddings", https://doi.org/10.18419/DARUS-4342, DaRUS, V1

This is a Pytorch implementation of the paper Ultrahyperbolic Knowledge Graph Embeddings published in KDD 2022. This code is used to reproduce the experiments of the method UltraE, a geometric embedding approach for knowledge graph embeddings. The code is tested on public datasets which can be downloaded from KGEmb. To execute the code, follow the...

Code for Pseudo-Riemannian Graph Convolutional Networks

Jul 5, 2024 - Analytic Computing

Xiong, Bo; Zhu, Shichao; Potyka, Nico; Pan, Shirui; Zhou, Chuan; Staab, Steffen, 2024, "Code for Pseudo-Riemannian Graph Convolutional Networks", https://doi.org/10.18419/DARUS-4340, DaRUS, V1, UNF:6:XC5GdaJdFoY7V7SNqvdoiQ== [fileUNF]

This dataset is the official implementation of Pseudo-Riemannian Graph Convolutional Networks in PyTorch, based on HGCN implementation. This code is used to reproduce the experiments of the paper. Datasets are provided in the /data directly. To execute the code, follow the instructions in the README.md file. For more info, please check the paper or...

KnowGraphs (EU)(Universität Stuttgart)

Jul 3, 2024Analytic Computing

Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/knowngraphs/

Hard Negative Captions(Universität Stuttgart)

Jul 3, 2024PN 6-5 (II)

PN 6-5 (II)(Universität Stuttgart)

Jul 3, 2024PN 6

SimTech Project PN 6-5 (II) "Interpretable and explainable cognitive inspired machine learning systems"

Code for training and using the droplet segmentation models

Jul 2, 2024 - Spray Segmentation

Jose, Basil; Hampp, Fabian, 2024, "Code for training and using the droplet segmentation models", https://doi.org/10.18419/DARUS-4147, DaRUS, V1

This dataset contains the necessary code for using our spray segmentation model used in the paper, Machine learning based spray process quantification. More information can be found in the README.md.

Dissertation Reiff(Universität Stuttgart)

Jul 1, 2024ISW Data Repository

Replication Data for: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers - Two Superfacility Case Studies

Jun 28, 2024 - Scientific Computing

Pollinger, Theresa; Van Craen, Alexander; Offenhäuser, Philipp, 2024, "Replication Data for: Realizing Joint Extreme-Scale Simulations on Multiple Supercomputers - Two Superfacility Case Studies", https://doi.org/10.18419/DARUS-3707, DaRUS, V1

This data repository contains input and output files for large-scale experiments conducted on the three German national supercomputers HAWK, SuperMUC-NG, and JUWELS. More structure can be seen when switching to "Tree" view below. Then, the files are structured into three folders (`io-benchmark`, `two-systems, `three-systems`), and the outputs were...

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