{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:RL5GYOUSCZ55PSBSN55LANKAPL","short_pith_number":"pith:RL5GYOUS","canonical_record":{"source":{"id":"2107.11085","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T08:53:46Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"24e013cb910daafba287eb558e7212563097712ca10234663a31096c6dac3234","abstract_canon_sha256":"a6663293ec6223b1e1cef3174cb5890ae1b87d8527fe2cda2cf3153e7f499c95"},"schema_version":"1.0"},"canonical_sha256":"8afa6c3a92167bd7c8326f7ab035407aff27e8a0cd0161d832d5a9a31d1d21c5","source":{"kind":"arxiv","id":"2107.11085","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.11085","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"arxiv_version","alias_value":"2107.11085v1","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11085","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_12","alias_value":"RL5GYOUSCZ55","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_16","alias_value":"RL5GYOUSCZ55PSBS","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_8","alias_value":"RL5GYOUS","created_at":"2026-07-05T03:00:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:RL5GYOUSCZ55PSBSN55LANKAPL","target":"record","payload":{"canonical_record":{"source":{"id":"2107.11085","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T08:53:46Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"24e013cb910daafba287eb558e7212563097712ca10234663a31096c6dac3234","abstract_canon_sha256":"a6663293ec6223b1e1cef3174cb5890ae1b87d8527fe2cda2cf3153e7f499c95"},"schema_version":"1.0"},"canonical_sha256":"8afa6c3a92167bd7c8326f7ab035407aff27e8a0cd0161d832d5a9a31d1d21c5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:00:13.120580Z","signature_b64":"ogIQ5GwLjY3/mTfLaW7C6W1YI+gGnnrmKVcpUXjhO3tqulJPgamkgWpnTjqGqo9jMxpID4E1mB9pl8Zz6295DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8afa6c3a92167bd7c8326f7ab035407aff27e8a0cd0161d832d5a9a31d1d21c5","last_reissued_at":"2026-07-05T03:00:13.120179Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:00:13.120179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2107.11085","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:00:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NY4QspLMxGcHB+ws4Vx8W8DQo1QqRY3mhevNBkrlQg5jiRdOmrsP+Z95FwOJtWlQuGsYRiGnTAGDJ/t44uq3AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:23:51.586348Z"},"content_sha256":"72837ec87c2fbc52b968a6075817678411539be75977d806b06bd4b33760eb7a","schema_version":"1.0","event_id":"sha256:72837ec87c2fbc52b968a6075817678411539be75977d806b06bd4b33760eb7a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:RL5GYOUSCZ55PSBSN55LANKAPL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-driven deep density estimation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Patrik Puchert, Pedro Hermosilla, Timo Ropinski, Tobias Ritschel","submitted_at":"2021-07-23T08:53:46Z","abstract_excerpt":"Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11085","kind":"arxiv","version":1},"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/2107.11085/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:00:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ouTQA6hZ2sCDY3vxGLGnwsbDD1OsQ0g1GuCnu0EJZDeErryxr9l6i+r2YDimi3vpNTDiEr8UFb6i6Xwd1/f0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T21:23:51.587767Z"},"content_sha256":"e907892dfeec30c2aaf3544993a44b6f7971b9097440ebaeb6484aaa10e207a3","schema_version":"1.0","event_id":"sha256:e907892dfeec30c2aaf3544993a44b6f7971b9097440ebaeb6484aaa10e207a3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RL5GYOUSCZ55PSBSN55LANKAPL/bundle.json","state_url":"https://pith.science/pith/RL5GYOUSCZ55PSBSN55LANKAPL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RL5GYOUSCZ55PSBSN55LANKAPL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T21:23:51Z","links":{"resolver":"https://pith.science/pith/RL5GYOUSCZ55PSBSN55LANKAPL","bundle":"https://pith.science/pith/RL5GYOUSCZ55PSBSN55LANKAPL/bundle.json","state":"https://pith.science/pith/RL5GYOUSCZ55PSBSN55LANKAPL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RL5GYOUSCZ55PSBSN55LANKAPL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:RL5GYOUSCZ55PSBSN55LANKAPL","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"a6663293ec6223b1e1cef3174cb5890ae1b87d8527fe2cda2cf3153e7f499c95","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T08:53:46Z","title_canon_sha256":"24e013cb910daafba287eb558e7212563097712ca10234663a31096c6dac3234"},"schema_version":"1.0","source":{"id":"2107.11085","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2107.11085","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"arxiv_version","alias_value":"2107.11085v1","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2107.11085","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_12","alias_value":"RL5GYOUSCZ55","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_16","alias_value":"RL5GYOUSCZ55PSBS","created_at":"2026-07-05T03:00:13Z"},{"alias_kind":"pith_short_8","alias_value":"RL5GYOUS","created_at":"2026-07-05T03:00:13Z"}],"graph_snapshots":[{"event_id":"sha256:e907892dfeec30c2aaf3544993a44b6f7971b9097440ebaeb6484aaa10e207a3","target":"graph","created_at":"2026-07-05T03:00:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2107.11085/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples. Thus, it is used in tasks as diverse as analyzing population data, spatial locations in 2D sensor readings, or reconstructing scenes from 3D scans. In this paper, we introduce a learned, data-driven deep density estimation (DDE) to infer PDFs in an accurate and efficient manner, while being independent of domain dimensionality or sample size. Furthermore, we do not require access to the original PDF during estimation, neither in parametric for","authors_text":"Patrik Puchert, Pedro Hermosilla, Timo Ropinski, Tobias Ritschel","cross_cats":["cs.CV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T08:53:46Z","title":"Data-driven deep density estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2107.11085","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:72837ec87c2fbc52b968a6075817678411539be75977d806b06bd4b33760eb7a","target":"record","created_at":"2026-07-05T03:00:13Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"a6663293ec6223b1e1cef3174cb5890ae1b87d8527fe2cda2cf3153e7f499c95","cross_cats_sorted":["cs.CV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2021-07-23T08:53:46Z","title_canon_sha256":"24e013cb910daafba287eb558e7212563097712ca10234663a31096c6dac3234"},"schema_version":"1.0","source":{"id":"2107.11085","kind":"arxiv","version":1}},"canonical_sha256":"8afa6c3a92167bd7c8326f7ab035407aff27e8a0cd0161d832d5a9a31d1d21c5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8afa6c3a92167bd7c8326f7ab035407aff27e8a0cd0161d832d5a9a31d1d21c5","first_computed_at":"2026-07-05T03:00:13.120179Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:00:13.120179Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ogIQ5GwLjY3/mTfLaW7C6W1YI+gGnnrmKVcpUXjhO3tqulJPgamkgWpnTjqGqo9jMxpID4E1mB9pl8Zz6295DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T03:00:13.120580Z","signed_message":"canonical_sha256_bytes"},"source_id":"2107.11085","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:72837ec87c2fbc52b968a6075817678411539be75977d806b06bd4b33760eb7a","sha256:e907892dfeec30c2aaf3544993a44b6f7971b9097440ebaeb6484aaa10e207a3"],"state_sha256":"fe4a9b054dcfb36e708f8c36ea31e986d1a0342b49995b13058cda81e727b3ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/V0gjW9q1tKwtAw4u5kLwvxKcAY2EAzfxnWXGzk+PQH9ZiR6JXC14L24FeGHO5UJizfKSRj6kRCRzovXCduxCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T21:23:51.621848Z","bundle_sha256":"57fa469b270a401fe119c50cf4b2304b46ab6e707a1e64cb01067b3ce99e8100"}}