{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:5NP33X5TDFS3A5E4PQJ323F7MF","short_pith_number":"pith:5NP33X5T","schema_version":"1.0","canonical_sha256":"eb5fbddfb31965b0749c7c13bd6cbf614954677460c860719781314e5805c78b","source":{"kind":"arxiv","id":"2306.00600","version":2},"attestation_state":"computed","paper":{"title":"Rotating Features for Object Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Francesco Locatello, Max Welling, Phillip Lippe, Sindy L\\\"owe","submitted_at":"2023-06-01T12:16:26Z","abstract_excerpt":"The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Ro"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.00600","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-06-01T12:16:26Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"fc27b3f4005cdd6cf6e07f1b29f89f43f6b504bdf095066b2f27856f4ebba148","abstract_canon_sha256":"53447aeabd1513476ba85aee3fa3eff31bf9ec6818ec593fecea7e6f12704e8a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:42.389723Z","signature_b64":"Li5GL+LQd9dk2qeNotP+z4o3aUFp9ZVXuuTqNjqI2ke0OIFHeel+ievzyVHtG/3CCjjj7XttIsjP7du07yeDDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"eb5fbddfb31965b0749c7c13bd6cbf614954677460c860719781314e5805c78b","last_reissued_at":"2026-07-05T07:01:42.389197Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:42.389197Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rotating Features for Object Discovery","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Francesco Locatello, Max Welling, Phillip Lippe, Sindy L\\\"owe","submitted_at":"2023-06-01T12:16:26Z","abstract_excerpt":"The binding problem in human cognition, concerning how the brain represents and connects objects within a fixed network of neural connections, remains a subject of intense debate. Most machine learning efforts addressing this issue in an unsupervised setting have focused on slot-based methods, which may be limiting due to their discrete nature and difficulty to express uncertainty. Recently, the Complex AutoEncoder was proposed as an alternative that learns continuous and distributed object-centric representations. However, it is only applicable to simple toy data. In this paper, we present Ro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.00600","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2306.00600/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.00600","created_at":"2026-07-05T07:01:42.389261+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.00600v2","created_at":"2026-07-05T07:01:42.389261+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.00600","created_at":"2026-07-05T07:01:42.389261+00:00"},{"alias_kind":"pith_short_12","alias_value":"5NP33X5TDFS3","created_at":"2026-07-05T07:01:42.389261+00:00"},{"alias_kind":"pith_short_16","alias_value":"5NP33X5TDFS3A5E4","created_at":"2026-07-05T07:01:42.389261+00:00"},{"alias_kind":"pith_short_8","alias_value":"5NP33X5T","created_at":"2026-07-05T07:01:42.389261+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.15900","citing_title":"Improving the Generation of VAEs with High Dimensional Latent Spaces by the use of Hyperspherical Coordinates","ref_index":14,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF","json":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF.json","graph_json":"https://pith.science/api/pith-number/5NP33X5TDFS3A5E4PQJ323F7MF/graph.json","events_json":"https://pith.science/api/pith-number/5NP33X5TDFS3A5E4PQJ323F7MF/events.json","paper":"https://pith.science/paper/5NP33X5T"},"agent_actions":{"view_html":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF","download_json":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF.json","view_paper":"https://pith.science/paper/5NP33X5T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.00600&json=true","fetch_graph":"https://pith.science/api/pith-number/5NP33X5TDFS3A5E4PQJ323F7MF/graph.json","fetch_events":"https://pith.science/api/pith-number/5NP33X5TDFS3A5E4PQJ323F7MF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF/action/storage_attestation","attest_author":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF/action/author_attestation","sign_citation":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF/action/citation_signature","submit_replication":"https://pith.science/pith/5NP33X5TDFS3A5E4PQJ323F7MF/action/replication_record"}},"created_at":"2026-07-05T07:01:42.389261+00:00","updated_at":"2026-07-05T07:01:42.389261+00:00"}