A small tool to view real-world ActivityPub objects as JSON! Enter a URL
or username from Mastodon or a similar service below, and we'll send a
request with
the right
Accept
header
to the server to view the underlying object.
{
"@context": [
"https://www.w3.org/ns/activitystreams",
{
"ostatus": "http://ostatus.org#",
"atomUri": "ostatus:atomUri",
"inReplyToAtomUri": "ostatus:inReplyToAtomUri",
"conversation": "ostatus:conversation",
"sensitive": "as:sensitive",
"toot": "http://joinmastodon.org/ns#",
"votersCount": "toot:votersCount",
"Hashtag": "as:Hashtag"
}
],
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415",
"type": "Note",
"summary": null,
"inReplyTo": null,
"published": "2023-01-23T13:01:00Z",
"url": "https://sigmoid.social/@rope/109738646610851415",
"attributedTo": "https://sigmoid.social/users/rope",
"to": [
"https://www.w3.org/ns/activitystreams#Public"
],
"cc": [
"https://sigmoid.social/users/rope/followers",
"https://sigmoid.social/users/leuvenai"
],
"sensitive": false,
"atomUri": "https://sigmoid.social/users/rope/statuses/109738646610851415",
"inReplyToAtomUri": null,
"conversation": "tag:sigmoid.social,2023-01-23:objectId=5344355:objectType=Conversation",
"content": "<p>I'm happy to share that our paper "Bayesian Structure Scores for Probabilistic Circuits" has been accepted at <a href=\"https://sigmoid.social/tags/aistats23\" class=\"mention hashtag\" rel=\"tag\">#<span>aistats23</span></a>! This is joint work with Yang Yang (MSc @mastodon.world, incoming PhD <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@leuvenai\" class=\"u-url mention\">@<span>leuvenai</span></a></span> and Gennaro Gala.</p><p>The main contribution of the paper is to propose a new (actually old) way to learn the structure of <a href=\"https://sigmoid.social/tags/ProbabilisticCircuits\" class=\"mention hashtag\" rel=\"tag\">#<span>ProbabilisticCircuits</span></a>. We take lessons from classical structure learning in Bayesian networks which optimizes some structure score, where a principled choice is a Bayesian score.</p>",
"contentMap": {
"en": "<p>I'm happy to share that our paper "Bayesian Structure Scores for Probabilistic Circuits" has been accepted at <a href=\"https://sigmoid.social/tags/aistats23\" class=\"mention hashtag\" rel=\"tag\">#<span>aistats23</span></a>! This is joint work with Yang Yang (MSc @mastodon.world, incoming PhD <span class=\"h-card\" translate=\"no\"><a href=\"https://sigmoid.social/@leuvenai\" class=\"u-url mention\">@<span>leuvenai</span></a></span> and Gennaro Gala.</p><p>The main contribution of the paper is to propose a new (actually old) way to learn the structure of <a href=\"https://sigmoid.social/tags/ProbabilisticCircuits\" class=\"mention hashtag\" rel=\"tag\">#<span>ProbabilisticCircuits</span></a>. We take lessons from classical structure learning in Bayesian networks which optimizes some structure score, where a principled choice is a Bayesian score.</p>"
},
"attachment": [],
"tag": [
{
"type": "Mention",
"href": "https://sigmoid.social/users/leuvenai",
"name": "@leuvenai"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/aistats23",
"name": "#aistats23"
},
{
"type": "Hashtag",
"href": "https://sigmoid.social/tags/probabilisticcircuits",
"name": "#probabilisticcircuits"
}
],
"replies": {
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies",
"type": "Collection",
"first": {
"type": "CollectionPage",
"next": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies?min_id=109738649308308193&page=true",
"partOf": "https://sigmoid.social/users/rope/statuses/109738646610851415/replies",
"items": [
"https://sigmoid.social/users/rope/statuses/109738649308308193"
]
}
},
"likes": {
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415/likes",
"type": "Collection",
"totalItems": 5
},
"shares": {
"id": "https://sigmoid.social/users/rope/statuses/109738646610851415/shares",
"type": "Collection",
"totalItems": 6
}
}