Changes

On December 2, 2024 at 10:24:54 PM UTC, admin:
Changed title to Synthetic Data (previously Synthetic data)
Set author of Synthetic Data to Gemma E. Moran (previously Yoeri Poels)
Updated description of Synthetic Data from
Synthetic data generated by following the similar setup as in [9], [29]. The data samples {x, y} on local node i are generated according to the model y = argmax(softmax(wx + b)), x ∈ R60, w ∈ R10×60, b ∈ R10. We set w, b ∼ N(0, 1). For the data on i.i.d. nodes, x follows the same distribution N(0, Σ), where Σ is diagonal with Σr, r = r−1.2. For the data samples on non-i.i.d. node i, x ∼ N(oi, Σ), each element in the mean vector oi is drawn from N(0, 1), oi ∼ N(0, χ).
to
The dataset used in the paper is a synthetic dataset for off-policy contextual bandits, with contexts x ∈ X, a finite set of actions A, and bounded real rewards r ∈ A → [0, 1].
Removed the following tags from Synthetic Data
Added the following tags to Synthetic Data
Changed value of field defined_in to https://doi.org/10.48550/arXiv.2012.04580 in Synthetic Data
Changed value of field extra_authors to [{'extra_author': 'Dhanya Sridhar', 'orcid': ''}, {'extra_author': 'Yixin Wang', 'orcid': ''}, {'extra_author': 'David M. Blei', 'orcid': ''}] in Synthetic Data
Changed value of field citation to ['https://doi.org/10.48550/arXiv.2401.02665', 'https://doi.org/10.48550/arXiv.2008.07720', 'https://doi.org/10.48550/arXiv.1906.03323', 'https://doi.org/10.48550/arXiv.2405.05430', 'https://doi.org/10.48550/arXiv.2110.10804', 'https://doi.org/10.48550/arXiv.2002.01599', 'https://doi.org/10.48550/arXiv.2206.05490', 'https://doi.org/10.48550/arXiv.1904.03483', 'https://doi.org/10.1016/j.neunet.2019.04.020', 'https://doi.org/10.48550/arXiv.2308.08321'] in Synthetic Data
Changed value of field landing_page to https://arxiv.org/abs/2202.03465 in Synthetic Data
Deleted resource Original Metadata from Synthetic Data
              
    
          
          
        
        
            f 1 { f 1 {
            2   "access_rights": "", 2   "access_rights": "",
            n 3   "author": "Yoeri Poels", n 3   "author": "Gemma E. Moran",
            4   "author_email": "", 4   "author_email": "",
            5   "citation": [ 5   "citation": [
            n 6     "https://doi.org/10.48550/arXiv.2004.11969", n 
            7     "https://doi.org/10.48550/arXiv.2108.13581", 
            8     "https://doi.org/10.48550/arXiv.1309.6847", 
            9     "https://doi.org/10.48550/arXiv.1502.03466", 6     "https://doi.org/10.48550/arXiv.2401.02665",
            7     "https://doi.org/10.48550/arXiv.2008.07720",
            8     "https://doi.org/10.48550/arXiv.1906.03323",
            9     "https://doi.org/10.48550/arXiv.2405.05430",
            10     "https://doi.org/10.48550/arXiv.1411.1804", 10     "https://doi.org/10.48550/arXiv.2110.10804",
            11     "https://doi.org/10.48550/arXiv.1703.02111", 
            12     "https://doi.org/10.48550/arXiv.2202.01841", 
            13     "https://doi.org/10.48550/arXiv.2003.03462", 
            14     "https://doi.org/10.48550/arXiv.2104.01714", 
            15     "https://doi.org/10.48550/arXiv.1611.00035", 
            16     "https://doi.org/10.48550/arXiv.1610.08749", 
            17     "https://doi.org/10.48550/arXiv.1609.08221", 
            18     "https://doi.org/10.48550/arXiv.1709.02357", 
            19     "https://doi.org/10.48550/arXiv.2105.07066", 
            20     "https://doi.org/10.48550/arXiv.2306.01204", 
            21     "https://doi.org/10.48550/arXiv.2108.09159" 11     "https://doi.org/10.48550/arXiv.2002.01599",
            12     "https://doi.org/10.48550/arXiv.2206.05490",
            13     "https://doi.org/10.48550/arXiv.1904.03483",
            14     "https://doi.org/10.1016/j.neunet.2019.04.020",
            15     "https://doi.org/10.48550/arXiv.2308.08321"
            22   ], 16   ],
            23   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700", 17   "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            n 24   "defined_in": "https://doi.org/10.48550/arXiv.1706.04646", n 18   "defined_in": "https://doi.org/10.48550/arXiv.2012.04580",
            25   "doi": "10.57702/ud58iifq", 19   "doi": "10.57702/ud58iifq",
            26   "doi_date_published": "2024-12-02", 20   "doi_date_published": "2024-12-02",
            27   "doi_publisher": "TIB", 21   "doi_publisher": "TIB",
            28   "doi_status": true, 22   "doi_status": true,
            29   "domain": "https://service.tib.eu/ldmservice", 23   "domain": "https://service.tib.eu/ldmservice",
            30   "extra_authors": [ 24   "extra_authors": [
            31     { 25     {
            n 32       "extra_author": "Vlado Menkovski", n 26       "extra_author": "Dhanya Sridhar",
            27       "orcid": ""
            28     },
            29     {
            30       "extra_author": "Yixin Wang",
            31       "orcid": ""
            32     },
            33     {
            34       "extra_author": "David M. Blei",
            33       "orcid": "" 35       "orcid": ""
            34     } 36     }
            35   ], 37   ],
            36   "groups": [ 38   "groups": [
            37     { 39     {
            38       "description": "", 40       "description": "",
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            41       "image_display_url": "", 43       "image_display_url": "",
            n 42       "name": "brain-imaging", n 44       "name": "data-augmentation",
            43       "title": "Brain Imaging" 45       "title": "Data Augmentation"
            44     }, 46     },
            45     { 47     {
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            51       "title": "Clustering" 53       "title": "Image Classification"
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            57       "image_display_url": "", 
            58       "name": "computer-vision", 
            59       "title": "Computer Vision" 
            60     }, 
            61     { 
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            63       "display_name": "Dimensionality Reduction", 
            64       "id": "331b1280-324e-4d4d-841d-8da03185ebdc", 
            65       "image_display_url": "", 
            66       "name": "dimensionality-reduction", 
            67       "title": "Dimensionality Reduction" 
            68     }, 
            69     { 
            70       "description": "", 
            71       "display_name": "Finite element simulation", 57       "display_name": "Image Segmentation",
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            73       "image_display_url": "", 59       "image_display_url": "",
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            75       "title": "Finite element simulation" 61       "title": "Image Segmentation"
            76     }, 62     },
            77     { 63     {
            78       "description": "", 64       "description": "",
            n 79       "display_name": "Gaussian Mixture Model", n 65       "display_name": "Location-Aware",
            80       "id": "300ec123-7e29-449b-a398-1174f59d391e", 66       "id": "9e7539ae-ddb5-4d79-956a-51a4536b89d4",
            81       "image_display_url": "", 67       "image_display_url": "",
            n 82       "name": "gaussian-mixture-model", n 68       "name": "location-aware",
            83       "title": "Gaussian Mixture Model" 69       "title": "Location-Aware"
            84     }, 
            85     { 
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            87       "display_name": "Graph Signal Processing", 
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            89       "image_display_url": "", 
            90       "name": "graph-signal-processing", 
            91       "title": "Graph Signal Processing" 
            92     }, 
            93     { 
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            95       "display_name": "Graphical Models", 
            96       "id": "349c2763-5e96-4584-88d9-79695e54c6be", 
            97       "image_display_url": "", 
            98       "name": "graphical-models", 
            99       "title": "Graphical Models" 
            100     }, 70     },
            101     { 71     {
            102       "description": "", 72       "description": "",
            103       "display_name": "Machine Learning", 73       "display_name": "Machine Learning",
            104       "id": "1d8623c9-adbd-4f91-be1e-53847c4ac32a", 74       "id": "1d8623c9-adbd-4f91-be1e-53847c4ac32a",
            105       "image_display_url": "", 75       "image_display_url": "",
            106       "name": "machine-learning", 76       "name": "machine-learning",
            107       "title": "Machine Learning" 77       "title": "Machine Learning"
            108     }, 78     },
            109     { 79     {
            110       "description": "", 80       "description": "",
            n 111       "display_name": "Multi-Label Classification", n 81       "display_name": "Off-Policy Contextual Bandits",
            112       "id": "6f9e0de4-7c26-404c-af3c-2f4cca3ff39b", 82       "id": "ef694ebd-912e-4c12-9234-afe5768d1b37",
            113       "image_display_url": "", 83       "image_display_url": "",
            n 114       "name": "multi-label-classification", n 84       "name": "off-policy-contextual-bandits",
            115       "title": "Multi-Label Classification" 85       "title": "Off-Policy Contextual Bandits"
            116     }, 86     },
            117     { 87     {
            118       "description": "", 88       "description": "",
            n 119       "display_name": "Robotics", n 89       "display_name": "Point Cloud Registration",
            120       "id": "31298f52-0c2b-478b-8b92-4817262302cb", 90       "id": "e398e0a7-92f5-41dd-ad91-6fd71b7c83c7",
            121       "image_display_url": "", 91       "image_display_url": "",
            n 122       "name": "robotics", n 92       "name": "point-cloud-registration",
            123       "title": "Robotics" 93       "title": "Point Cloud Registration"
            124     }, 94     },
            125     { 95     {
            126       "description": "", 96       "description": "",
            n 127       "display_name": "Simulation", n 97       "display_name": "Question Answering",
            128       "id": "b5e2d9c7-7af3-484f-a5b9-bc9a435bfde4", 98       "id": "01e9bd6b-910d-421d-bb31-799604023211",
            129       "image_display_url": "", 99       "image_display_url": "",
            n 130       "name": "simulation", n 100       "name": "question-answering",
            131       "title": "Simulation" 101       "title": "Question Answering"
            132     }, 102     },
            133     { 103     {
            134       "description": "", 104       "description": "",
            135       "display_name": "Synthetic data", 105       "display_name": "Synthetic data",
            136       "id": "811b488a-c719-4335-b1f5-20ffcb5b1bb4", 106       "id": "811b488a-c719-4335-b1f5-20ffcb5b1bb4",
            137       "image_display_url": "", 107       "image_display_url": "",
            138       "name": "synthetic-data", 108       "name": "synthetic-data",
            139       "title": "Synthetic data" 109       "title": "Synthetic data"
            140     }, 110     },
            141     { 111     {
            142       "description": "", 112       "description": "",
            n n 113       "display_name": "Synthetic data generation",
            114       "id": "366cfe42-f42f-432c-9315-8aedd48ac429",
            115       "image_display_url": "",
            116       "name": "synthetic-data-generation",
            117       "title": "Synthetic data generation"
            118     },
            119     {
            120       "description": "",
            143       "display_name": "Time Series Analysis", 121       "display_name": "Time Series Forecasting",
            144       "id": "c0b2d680-8a2d-4a70-8cea-3de77e468832", 122       "id": "2d0040ff-e50c-4b62-a5dc-c4c0241f05f1",
            145       "image_display_url": "", 123       "image_display_url": "",
            n 146       "name": "time-series-analysis", n 124       "name": "time-series-forecasting",
            147       "title": "Time Series Analysis" 125       "title": "Time Series Forecasting"
            148     }, 
            149     { 
            150       "description": "", 
            151       "display_name": "Uncertainty quantification", 
            152       "id": "2e31c34a-e34c-465a-a766-85452f4de334", 
            153       "image_display_url": "", 
            154       "name": "uncertainty-quantification", 
            155       "title": "Uncertainty quantification" 
            156     } 126     }
            157   ], 127   ],
            158   "id": "194206d2-4153-4a97-b3d0-a949797bb5a9", 128   "id": "194206d2-4153-4a97-b3d0-a949797bb5a9",
            159   "isopen": false, 129   "isopen": false,
            n 160   "landing_page": "https://github.com/yoeripoels/vce", n 130   "landing_page": "https://arxiv.org/abs/2202.03465",
            161   "license_title": null, 131   "license_title": null,
            162   "link_orkg": "", 132   "link_orkg": "",
            163   "metadata_created": "2024-12-02T22:19:28.615127", 133   "metadata_created": "2024-12-02T22:19:28.615127",
            n 164   "metadata_modified": "2024-12-02T22:19:29.316475", n 134   "metadata_modified": "2024-12-02T22:24:53.447029",
            165   "name": "synthetic-data", 135   "name": "synthetic-data",
            n 166   "notes": "Synthetic data generated by following the similar setup as  n 136   "notes": "The dataset used in the paper is a synthetic dataset for 
            167 in [9], [29]. The data samples {x, y} on local node i are generated  137 off-policy contextual bandits, with contexts x \u2208 X, a finite set 
            168 according to the model y = argmax(softmax(wx + b)), x \u2208 R60, w  138 of actions A, and bounded real rewards r \u2208 A \u2192 [0, 1].",
            169 \u2208 R10\u00d760, b \u2208 R10. We set w, b \u223c N(0, 1). For the  
            170 data on i.i.d. nodes, x follows the same distribution N(0, \u03a3),  
            171 where \u03a3 is diagonal with \u03a3r, r = r\u22121.2. For the data  
            172 samples on non-i.i.d. node i, x \u223c N(oi, \u03a3), each element in  
            173 the mean vector oi is drawn from N(0, 1), oi \u223c N(0, \u03c7).", 
            174   "num_resources": 1, 139   "num_resources": 0,
            175   "num_tags": 36, 140   "num_tags": 27,
            176   "organization": { 141   "organization": {
            177     "approval_status": "approved", 142     "approval_status": "approved",
            178     "created": "2024-11-25T12:11:38.292601", 143     "created": "2024-11-25T12:11:38.292601",
            179     "description": "", 144     "description": "",
            180     "id": "079d46db-32df-4b48-91f3-0a8bc8f69559", 145     "id": "079d46db-32df-4b48-91f3-0a8bc8f69559",
            181     "image_url": "", 146     "image_url": "",
            182     "is_organization": true, 147     "is_organization": true,
            183     "name": "no-organization", 148     "name": "no-organization",
            184     "state": "active", 149     "state": "active",
            185     "title": "No Organization", 150     "title": "No Organization",
            186     "type": "organization" 151     "type": "organization"
            187   }, 152   },
            188   "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559", 153   "owner_org": "079d46db-32df-4b48-91f3-0a8bc8f69559",
            189   "private": false, 154   "private": false,
            190   "relationships_as_object": [], 155   "relationships_as_object": [],
            191   "relationships_as_subject": [], 156   "relationships_as_subject": [],
            n 192   "resources": [ n 157   "resources": [],
            193     { 
            194       "cache_last_updated": null, 
            195       "cache_url": null, 
            196       "created": "2024-12-02T22:29:38", 
            197       "data": [ 
            198         "dcterms:title", 
            199         "dcterms:accessRights", 
            200         "dcterms:creator", 
            201         "dcterms:description", 
            202         "dcterms:issued", 
            203         "dcterms:language", 
            204         "dcterms:identifier", 
            205         "dcat:theme", 
            206         "dcterms:type", 
            207         "dcat:keyword", 
            208         "dcat:landingPage", 
            209         "dcterms:hasVersion", 
            210         "dcterms:format", 
            211         "mls:task", 
            212         "datacite:isDescribedBy" 
            213       ], 
            214       "description": "The json representation of the dataset with its  
            215 distributions based on DCAT.", 
            216       "format": "JSON", 
            217       "hash": "", 
            218       "id": "8b72ad3b-76af-4681-aa5e-c18eb11de989", 
            219       "last_modified": "2024-12-02T22:19:29.303517", 
            220       "metadata_modified": "2024-12-02T22:19:29.319373", 
            221       "mimetype": "application/json", 
            222       "mimetype_inner": null, 
            223       "name": "Original Metadata", 
            224       "package_id": "194206d2-4153-4a97-b3d0-a949797bb5a9", 
            225       "position": 0, 
            226       "resource_type": null, 
            227       "size": 2989, 
            228       "state": "active", 
            229       "url":  
            230 resource/8b72ad3b-76af-4681-aa5e-c18eb11de989/download/metadata.json", 
            231       "url_type": "upload" 
            232     } 
            233   ], 
            234   "services_used_list": "", 158   "services_used_list": "",
            235   "state": "active", 159   "state": "active",
            236   "tags": [ 160   "tags": [
            237     { 161     {
            n n 162       "display_name": "Bayesian Networks",
            163       "id": "b9b7d27f-fe47-490f-b71d-e6e34485df13",
            164       "name": "Bayesian Networks",
            165       "state": "active",
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            168     {
            169       "display_name": "Correspondences",
            170       "id": "45449dc7-175d-4a96-8bd0-3d6e637a06fb",
            171       "name": "Correspondences",
            172       "state": "active",
            173       "vocabulary_id": null
            174     },
            175     {
            238       "display_name": "Beta", 176       "display_name": "DMD",
            239       "id": "a8d59c98-fb28-4de2-95c5-245773890ba4", 177       "id": "3463bd71-0577-4d45-ac27-055b59dd6a53",
            240       "name": "Beta", 178       "name": "DMD",
            241       "state": "active", 179       "state": "active",
            242       "vocabulary_id": null 180       "vocabulary_id": null
            243     }, 181     },
            244     { 182     {
            n 245       "display_name": "Copy memory problem", n 183       "display_name": "Data Augmentation",
            246       "id": "6794115b-4bf7-48b9-a283-be3f78e42cf9", 184       "id": "9357b14b-40d7-4e6a-b7d8-fb3a9b710336",
            247       "name": "Copy memory problem", 185       "name": "Data Augmentation",
            248       "state": "active", 186       "state": "active",
            249       "vocabulary_id": null 187       "vocabulary_id": null
            250     }, 188     },
            251     { 189     {
            n 252       "display_name": "Differential Privacy", n 
            253       "id": "9cb11303-6bc7-470b-8b52-3f265afce75a", 
            254       "name": "Differential Privacy", 
            255       "state": "active", 
            256       "vocabulary_id": null 
            257     }, 
            258     { 
            259       "display_name": "Feature-level clustering", 
            260       "id": "29480a2e-ba23-44ab-bf9d-0f196af07056", 
            261       "name": "Feature-level clustering", 
            262       "state": "active", 
            263       "vocabulary_id": null 
            264     }, 
            265     { 
            266       "display_name": "Gamma", 190       "display_name": "GANs",
            267       "id": "463f98c4-5dcc-4709-869e-e21e9dad8f10", 191       "id": "ba92b867-41e4-435d-b0d6-285954f842d8",
            268       "name": "Gamma", 192       "name": "GANs",
            269       "state": "active", 193       "state": "active",
            270       "vocabulary_id": null 194       "vocabulary_id": null
            271     }, 195     },
            272     { 196     {
            n 273       "display_name": "Gaussian Mixture Model", n 
            274       "id": "afe1dc28-0693-480d-be08-a3ef2c9eb23a", 
            275       "name": "Gaussian Mixture Model", 
            276       "state": "active", 
            277       "vocabulary_id": null 
            278     }, 
            279     { 
            280       "display_name": "Gaussian process", 
            281       "id": "a547b3a1-01da-4731-a5a2-f92a9a667281", 
            282       "name": "Gaussian process", 
            283       "state": "active", 
            284       "vocabulary_id": null 
            285     }, 
            286     { 
            287       "display_name": "Graphical Models", 197       "display_name": "Generative Models",
            288       "id": "26d62a69-71de-499d-9b39-a04023fe0e46", 198       "id": "dafd9c1f-394f-4d88-9c07-5801683e057a",
            289       "name": "Graphical Models", 199       "name": "Generative Models",
            290       "state": "active", 200       "state": "active",
            291       "vocabulary_id": null 201       "vocabulary_id": null
            292     }, 202     },
            293     { 203     {
            n n 204       "display_name": "Image Classification",
            205       "id": "418e2ddf-a1d3-42ac-ad05-156f79ca8e22",
            206       "name": "Image Classification",
            207       "state": "active",
            208       "vocabulary_id": null
            209     },
            210     {
            211       "display_name": "Image segmentation",
            212       "id": "8ab34344-1929-4cd3-a55a-af732b0cf13b",
            213       "name": "Image segmentation",
            214       "state": "active",
            215       "vocabulary_id": null
            216     },
            217     {
            218       "display_name": "Linear Models",
            219       "id": "70606ecb-b0c8-40c1-aa56-fb0af35ad8ec",
            220       "name": "Linear Models",
            221       "state": "active",
            222       "vocabulary_id": null
            223     },
            224     {
            225       "display_name": "Location-Aware",
            226       "id": "e7ab05fe-a918-4c3e-9bf0-fc54e3550705",
            227       "name": "Location-Aware",
            228       "state": "active",
            229       "vocabulary_id": null
            230     },
            231     {
            294       "display_name": "Machine learning", 232       "display_name": "Machine Learning",
            295       "id": "7c8050e7-2e86-4a96-aebe-6c582621940d", 233       "id": "c4f3defc-ca48-45a9-9217-ce35bd3ed73c",
            296       "name": "Machine learning", 234       "name": "Machine Learning",
            297       "state": "active", 235       "state": "active",
            298       "vocabulary_id": null 236       "vocabulary_id": null
            299     }, 237     },
            300     { 238     {
            n 301       "display_name": "Multi-label classification", n 239       "display_name": "Object Masking",
            302       "id": "d4e5a262-a723-47a7-935a-c733134de173", 240       "id": "0b16400f-e7de-411f-8eaa-90ebd5a94f54",
            303       "name": "Multi-label classification", 241       "name": "Object Masking",
            304       "state": "active", 242       "state": "active",
            305       "vocabulary_id": null 243       "vocabulary_id": null
            306     }, 244     },
            307     { 245     {
            n n 246       "display_name": "Ornstein-Uhlenbeck process",
            247       "id": "03f0385a-d99d-48e2-ab6f-6afa82b54ac6",
            248       "name": "Ornstein-Uhlenbeck process",
            249       "state": "active",
            250       "vocabulary_id": null
            251     },
            252     {
            308       "display_name": "Poisson", 253       "display_name": "Point Cloud",
            309       "id": "2b7a38e0-c7a2-48a7-8e72-c7e342d85b62", 254       "id": "23509753-62ca-44b6-adcc-aa97e99d8cee",
            310       "name": "Poisson", 255       "name": "Point Cloud",
            311       "state": "active", 256       "state": "active",
            312       "vocabulary_id": null 257       "vocabulary_id": null
            313     }, 258     },
            314     { 259     {
            n n 260       "display_name": "Potts model",
            261       "id": "8de02c1d-7298-4cbe-98ad-755e512c0a67",
            262       "name": "Potts model",
            263       "state": "active",
            264       "vocabulary_id": null
            265     },
            266     {
            315       "display_name": "Simulation", 267       "display_name": "Registration",
            316       "id": "0eafcef7-9068-449f-9656-4caa6fae3f8f", 268       "id": "d7844ba8-947a-42cc-b2d4-c388413ca623",
            317       "name": "Simulation", 269       "name": "Registration",
            318       "state": "active", 270       "state": "active",
            319       "vocabulary_id": null 271       "vocabulary_id": null
            320     }, 272     },
            321     { 273     {
            322       "display_name": "Synthetic Data", 274       "display_name": "Synthetic Data",
            323       "id": "3acddc74-a4df-4bb0-8962-ea0a27c72454", 275       "id": "3acddc74-a4df-4bb0-8962-ea0a27c72454",
            324       "name": "Synthetic Data", 276       "name": "Synthetic Data",
            325       "state": "active", 277       "state": "active",
            326       "vocabulary_id": null 278       "vocabulary_id": null
            327     }, 279     },
            328     { 280     {
            n n 281       "display_name": "Synthetic Data Generation",
            282       "id": "75d9cd3d-3561-4dc5-b53f-1587ae9d23a1",
            283       "name": "Synthetic Data Generation",
            284       "state": "active",
            285       "vocabulary_id": null
            286     },
            287     {
            329       "display_name": "Synthetic data", 288       "display_name": "Synthetic data",
            330       "id": "61b714ce-435e-4f7d-89c8-d37aa9d1c178", 289       "id": "61b714ce-435e-4f7d-89c8-d37aa9d1c178",
            331       "name": "Synthetic data", 290       "name": "Synthetic data",
            332       "state": "active", 291       "state": "active",
            333       "vocabulary_id": null 292       "vocabulary_id": null
            334     }, 293     },
            335     { 294     {
            n 336       "display_name": "System identi\ufb01cation", n 
            337       "id": "0220d15d-66db-40f2-938c-ddf9b5c8c71b", 
            338       "name": "System identi\ufb01cation", 
            339       "state": "active", 
            340       "vocabulary_id": null 
            341     }, 
            342     { 
            343       "display_name": "TSC algorithm", 
            344       "id": "43c5047b-fa7e-4735-928b-d18798ea9b75", 
            345       "name": "TSC algorithm", 
            346       "state": "active", 
            347       "vocabulary_id": null 
            348     }, 
            349     { 
            350       "display_name": "Time series", 295       "display_name": "Time Series",
            351       "id": "8707448c-d5d3-45e3-be91-98cf58577d77", 296       "id": "cb3036eb-2914-4e95-9ed3-111e2f4d13d6",
            352       "name": "Time series", 297       "name": "Time Series",
            353       "state": "active", 298       "state": "active",
            354       "vocabulary_id": null 299       "vocabulary_id": null
            355     }, 300     },
            356     { 301     {
            n 357       "display_name": "Translation-invariant", n 
            358       "id": "af1f21d8-e25f-4b36-a7bb-af8c23f8e7c8", 
            359       "name": "Translation-invariant", 
            360       "state": "active", 
            361       "vocabulary_id": null 
            362     }, 
            363     { 
            364       "display_name": "Tree structured models", 
            365       "id": "422b2786-2d6d-4ff9-9056-3a6116cc0573", 
            366       "name": "Tree structured models", 
            367       "state": "active", 
            368       "vocabulary_id": null 
            369     }, 
            370     { 
            371       "display_name": "computer vision", 
            372       "id": "f650b4e3-9955-49b0-ba7b-2d302a990978", 
            373       "name": "computer vision", 
            374       "state": "active", 
            375       "vocabulary_id": null 
            376     }, 
            377     { 
            378       "display_name": "concept", 
            379       "id": "5a907301-68eb-410b-ba16-d91422e754b1", 
            380       "name": "concept", 
            381       "state": "active", 
            382       "vocabulary_id": null 
            383     }, 
            384     { 
            385       "display_name": "dataset", 302       "display_name": "VAEs",
            386       "id": "ce5ad030-ca3d-47e6-abd1-5c92a2806f1b", 303       "id": "bc4f8f0a-27bd-4027-92ff-f944fd68f0fd",
            387       "name": "dataset", 304       "name": "VAEs",
            388       "state": "active", 305       "state": "active",
            389       "vocabulary_id": null 306       "vocabulary_id": null
            390     }, 307     },
            391     { 308     {
            n 392       "display_name": "finite element simulation", n 309       "display_name": "contextual bandits",
            393       "id": "6cd4815c-5dcb-499a-812e-95d12f16e171", 310       "id": "7a31b39c-4847-47a6-ba5d-caea872f0a9f",
            394       "name": "finite element simulation", 311       "name": "contextual bandits",
            395       "state": "active", 312       "state": "active",
            396       "vocabulary_id": null 313       "vocabulary_id": null
            397     }, 314     },
            398     { 315     {
            n 399       "display_name": "graph-smooth", n 
            400       "id": "ac93cf8a-8936-4a49-89ee-95b0c2ce742c", 
            401       "name": "graph-smooth", 
            402       "state": "active", 
            403       "vocabulary_id": null 
            404     }, 
            405     { 
            406       "display_name": "grossly-corrupted", 
            407       "id": "4f09d708-c716-4f54-bc9f-f11bf97399fd", 
            408       "name": "grossly-corrupted", 
            409       "state": "active", 
            410       "vocabulary_id": null 
            411     }, 
            412     { 
            413       "display_name": "heterogeneous material parameters", 
            414       "id": "e497aa01-952f-4937-880e-e265d7071c85", 
            415       "name": "heterogeneous material parameters", 
            416       "state": "active", 
            417       "vocabulary_id": null 
            418     }, 
            419     { 
            420       "display_name": "high-dimensional", 
            421       "id": "ed657af3-11f9-4ec1-b421-2d4c35bb93b8", 
            422       "name": "high-dimensional", 
            423       "state": "active", 
            424       "vocabulary_id": null 
            425     }, 
            426     { 
            427       "display_name": "i.i.d. data", 
            428       "id": "17e7e770-36fe-43e4-b2aa-683ab5e8bd55", 
            429       "name": "i.i.d. data", 
            430       "state": "active", 
            431       "vocabulary_id": null 
            432     }, 
            433     { 
            434       "display_name": "image", 
            435       "id": "2750bf30-5ae4-4ae8-bfef-5a168733376b", 
            436       "name": "image", 
            437       "state": "active", 
            438       "vocabulary_id": null 
            439     }, 
            440     { 
            441       "display_name": "machine learning", 
            442       "id": "9e42784b-6ee7-47e8-a69a-28b8c510212b", 
            443       "name": "machine learning", 
            444       "state": "active", 
            445       "vocabulary_id": null 
            446     }, 
            447     { 
            448       "display_name": "non-homogeneous Poisson process", 
            449       "id": "17b5862b-c763-4693-a700-7db8d83008bd", 
            450       "name": "non-homogeneous Poisson process", 
            451       "state": "active", 
            452       "vocabulary_id": null 
            453     }, 
            454     { 
            455       "display_name": "non-i.i.d. data", 
            456       "id": "4953944a-c449-4360-ab9b-329f27138f2b", 
            457       "name": "non-i.i.d. data", 
            458       "state": "active", 
            459       "vocabulary_id": null 
            460     }, 
            461     { 
            462       "display_name": "robotics", 316       "display_name": "off-policy",
            463       "id": "67a95a0a-dadc-477b-914a-0d659a29d2b7", 317       "id": "db7e595d-861d-41e1-a918-765755ed3383",
            464       "name": "robotics", 318       "name": "off-policy",
            319       "state": "active",
            320       "vocabulary_id": null
            321     },
            322     {
            323       "display_name": "question answering",
            324       "id": "d4e35d0b-ca07-4d05-bb27-50b2f30f2f00",
            325       "name": "question answering",
            326       "state": "active",
            327       "vocabulary_id": null
            328     },
            329     {
            330       "display_name": "single entity",
            331       "id": "96a6dc93-4c5c-41bd-a3de-4c6eb347e168",
            332       "name": "single entity",
            333       "state": "active",
            334       "vocabulary_id": null
            335     },
            336     {
            337       "display_name": "single relation",
            338       "id": "f4b0bddd-8fab-44fb-8f93-1a8aea1a23ab",
            339       "name": "single relation",
            465       "state": "active", 340       "state": "active",
            466       "vocabulary_id": null 341       "vocabulary_id": null
            467     }, 342     },
            468     { 343     {
            469       "display_name": "synthetic data", 344       "display_name": "synthetic data",
            470       "id": "f08e3559-1ee2-4a31-adc3-f9ecfa5fe045", 345       "id": "f08e3559-1ee2-4a31-adc3-f9ecfa5fe045",
            471       "name": "synthetic data", 346       "name": "synthetic data",
            472       "state": "active", 347       "state": "active",
            473       "vocabulary_id": null 348       "vocabulary_id": null
            n 474     }, n 
            475     { 
            476       "display_name": "uncertainty quantification", 
            477       "id": "db917de6-5010-47c6-86a8-9796d5f3ca88", 
            478       "name": "uncertainty quantification", 
            479       "state": "active", 
            480       "vocabulary_id": null 
            481     }, 
            482     { 
            483       "display_name": "variances", 
            484       "id": "bd86f97c-b464-40a1-bcba-f3ff9ae69658", 
            485       "name": "variances", 
            486       "state": "active", 
            487       "vocabulary_id": null 
            488     } 349     }
            489   ], 350   ],
            t 490   "title": "Synthetic data", t 351   "title": "Synthetic Data",
            491   "type": "dataset", 352   "type": "dataset",
            492   "version": "" 353   "version": ""
            493 } 354 }