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  "access_rights": "",
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  "author": "Yuxin Tang",
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  "author": "Yuxin Tang",
            
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  "author_email": "",
            
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  "doi": "10.57702/zakgk2ok",
            
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  "doi_date_published": "2024-12-16",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Zhimin Ding",
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      "extra_author": "Zhimin Ding",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Dimitrije Jankov",
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      "extra_author": "Dimitrije Jankov",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Binhang Yuan",
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      "extra_author": "Binhang Yuan",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Daniel Bourgeois",
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      "extra_author": "Daniel Bourgeois",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Chris Jermaine",
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      "extra_author": "Chris Jermaine",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "title": "Graph Convolutional Networks"
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      "title": "Graph Convolutional Networks"
            
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      "title": "Knowledge Graph Embedding"
            
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      "description": "",
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      "name": "non-negative-matrix-factorization",
            
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      "title": "Non-negative matrix factorization"
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      "title": "Non-negative matrix factorization"
            
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  "name": 
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ion-of-relational-computations-for-very-large-scale-machine-learning",
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ion-of-relational-computations-for-very-large-scale-machine-learning",
            
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  "notes": "The relational data model was designed to facilitate 
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  "notes": "The relational data model was designed to facilitate 
            
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large-scale data management and analytics. We consider the problem of 
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large-scale data management and analytics. We consider the problem of 
            
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how to differentiate computations expressed relationally.",
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how to differentiate computations expressed relationally.",
            
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distributions based on DCAT.",
            
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    {
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    {
            
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      "display_name": "graph convolutional networks",
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      "display_name": "graph convolutional networks",
            
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      "id": "7779c845-de19-4500-9080-bf562609769b",
            
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      "name": "graph convolutional networks",
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      "display_name": "knowledge graph embedding",
            
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      "id": "df56ef8b-651d-484d-b8cf-d83cefad17e6",
            
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      "name": "knowledge graph embedding",
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      "name": "knowledge graph embedding",
            
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    },
            
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    {
            
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      "display_name": "non-negative matrix factorization",
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      "display_name": "non-negative matrix factorization",
            
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      "id": "fe7360dd-cd8f-49f5-842f-2283529c0bda",
            
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      "name": "non-negative matrix factorization",
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      "name": "non-negative matrix factorization",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Auto-Differentiation of Relational Computations for Very 
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  "title": "Auto-Differentiation of Relational Computations for Very 
            
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Large Scale Machine Learning",
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Large Scale Machine Learning",
            
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  "version": ""
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  "version": ""
            
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}
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}