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Subject: Computer and Information Science

61 to 70 of 84 Results

DSAC++ Visual Camera Re-Localization [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "DSAC++ Visual Camera Re-Localization [Data]", https://doi.org/10.11588/data/EGCMUU, heiDATA, V1 Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the Cambridge Landmarks dataset. We also provide pre-trained models of our method for the MSR 7Scenes dataset and the Cambridge Landmarks dataset. For mo...
DSAC* Visual Re-Localization [Data] Jan 7, 2022 - IWR Visual Learning Lab Brachmann, Eric, 2020, "DSAC* Visual Re-Localization [Data]", https://doi.org/10.11588/data/N07HKC, heiDATA, V2 Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the MSR 7Scenes dataset, and the Stanford 12Scenes dataset, as well as precomputed camera coordinate files for both aforementioned datasets. For more inf...
Differentiable RANSAC (DSAC) for Visual Re-Localization [Data] Sep 7, 2020 - IWR Visual Learning Lab Brachmann, Eric, 2020, "Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/3JVZSH, heiDATA, V1 Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC
DeModify Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Nastase, Vivi; Fritz, Devon; Frank, Anette, 2019, "DeModify", https://doi.org/10.11588/data/KIWEMF, heiDATA, V1 deModify consists of 3631 instances, each with three annotations obtained through CrowdFlower. An instance is a short story in which a modifier is annotated with respect to its impact on the information in the story, assessed through its deletion from the context: crucial, not-cr...
Datasets for Dependency Tree Reranking Nov 13, 2023 - Neural Techniques for German Dependency Parsing Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1 This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation".
COREC – A neural multi-label COmmonsense RElation Classification system Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo) Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1 We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...
Converter for content-to-head style syntactic dependencies Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo) Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1 A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).
Collagen breaks at weak sacrificial bonds taming its mechanoradicals [Data] Mar 24, 2023 - HITS MBM Rennekamp, Benedikt; Gräter, Frauke, 2023, "Collagen breaks at weak sacrificial bonds taming its mechanoradicals [Data]", https://doi.org/10.11588/data/HJ6SVM, heiDATA, V1, UNF:6:Da6omTKjrSvNcDwyJxuHOg== [fileUNF] This dataset contains input files for MD simulations, derived breakage counts from these simulations that are used to generate the figures in the publication, and the experimental, uncropped SDS-PAGE gels of the presented results in the related publication. Abstract of related pu...
CoCo-Ex Feb 26, 2024 - RATIO_EXPLAIN Becker, Maria, 2024, "CoCo-Ex", https://doi.org/10.11588/data/K8MCIW, heiDATA, V1 CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.
CO-NNECT Feb 26, 2024 - RATIO_EXPLAIN Becker, Maria, 2024, "CO-NNECT", https://doi.org/10.11588/data/SAJAD3, heiDATA, V1 This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classificatio...

DSAC++ Visual Camera Re-Localization [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "DSAC++ Visual Camera Re-Localization [Data]", https://doi.org/10.11588/data/EGCMUU, heiDATA, V1

Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the Cambridge Landmarks dataset. We also provide pre-trained models of our method for the MSR 7Scenes dataset and the Cambridge Landmarks dataset. For mo...

DSAC* Visual Re-Localization [Data]

Jan 7, 2022 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "DSAC* Visual Re-Localization [Data]", https://doi.org/10.11588/data/N07HKC, heiDATA, V2

Supplementary training data for visual camera re-localization, particularly rendered depth maps to be used in combination with the MSR 7Scenes dataset, and the Stanford 12Scenes dataset, as well as precomputed camera coordinate files for both aforementioned datasets. For more inf...

Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]

Sep 7, 2020 - IWR Visual Learning Lab

Brachmann, Eric, 2020, "Differentiable RANSAC (DSAC) for Visual Re-Localization [Data]", https://doi.org/10.11588/data/3JVZSH, heiDATA, V1

Pre-trained models of our camera re-localization method for the MSR 7Scenes dataset. For more information, also see the code documentation: https://github.com/cvlab-dresden/DSAC

DeModify

Jul 15, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Nastase, Vivi; Fritz, Devon; Frank, Anette, 2019, "DeModify", https://doi.org/10.11588/data/KIWEMF, heiDATA, V1

deModify consists of 3631 instances, each with three annotations obtained through CrowdFlower. An instance is a short story in which a modifier is annotated with respect to its impact on the information in the story, assessed through its deletion from the context: crucial, not-cr...

Datasets for Dependency Tree Reranking

Nov 13, 2023 - Neural Techniques for German Dependency Parsing

Do, Bich-Ngoc; Rehbein, Ines, 2023, "Datasets for Dependency Tree Reranking", https://doi.org/10.11588/data/E5NOYH, heiDATA, V1

This resource contains the datasets for dependency tree reranking in 3 languages: English, German and Czech. The creation, analysis and experiment results of the datasets are described in the paper: Do and Rehbein (2020). "Neural Reranking for Dependency Parsing: An Evaluation".

COREC – A neural multi-label COmmonsense RElation Classification system

Oct 22, 2019 - Empirical Linguistics and Computational Language Modeling (LiMo)

Becker, Maria, 2019, "COREC – A neural multi-label COmmonsense RElation Classification system", https://doi.org/10.11588/data/E5EHBV, heiDATA, V1

We examine the learnability of Commonsense knowledge relations as represented in CONCEPTNET. We develop a neural open world multi-label classification system that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the spec...

Converter for content-to-head style syntactic dependencies

Mar 26, 2020 - Empirical Linguistics and Computational Language Modeling (LiMo)

Rehbein, Ines; Steen, Julius; Do, Bich-Ngoc; Frank, Anette, 2020, "Converter for content-to-head style syntactic dependencies", https://doi.org/10.11588/data/HE3BAZ, heiDATA, V1

A set of Python scripts that convert function-head style encodings in dependency treebanks in a content-head style encoding (as used in the UD treebanks) and vice versa (for adpositions, copula and coordination). For more information, see (Rehbein, Steen, Do & Frank 2017).

Collagen breaks at weak sacrificial bonds taming its mechanoradicals [Data]

Mar 24, 2023 - HITS MBM

Rennekamp, Benedikt; Gräter, Frauke, 2023, "Collagen breaks at weak sacrificial bonds taming its mechanoradicals [Data]", https://doi.org/10.11588/data/HJ6SVM, heiDATA, V1, UNF:6:Da6omTKjrSvNcDwyJxuHOg== [fileUNF]

This dataset contains input files for MD simulations, derived breakage counts from these simulations that are used to generate the figures in the publication, and the experimental, uncropped SDS-PAGE gels of the presented results in the related publication. Abstract of related pu...

CoCo-Ex

Feb 26, 2024 - RATIO_EXPLAIN

Becker, Maria, 2024, "CoCo-Ex", https://doi.org/10.11588/data/K8MCIW, heiDATA, V1

CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.

CO-NNECT

Feb 26, 2024 - RATIO_EXPLAIN

Becker, Maria, 2024, "CO-NNECT", https://doi.org/10.11588/data/SAJAD3, heiDATA, V1

This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classificatio...

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