{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:EOWIRCVRHPNWSOQ2BOCJRUGER4","short_pith_number":"pith:EOWIRCVR","canonical_record":{"source":{"id":"2007.07367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-14T21:25:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ce10156c689430a2e40a16622a295974a9a9404c8f830cef36d11e3a4478fa42","abstract_canon_sha256":"855d180f2c599fb3177095e5dde72a24e4fc7b98334d2d8c56088acdba0706d8"},"schema_version":"1.0"},"canonical_sha256":"23ac888ab13bdb693a1a0b8498d0c48f105c06feb03102707b52836836cc300b","source":{"kind":"arxiv","id":"2007.07367","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.07367","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"arxiv_version","alias_value":"2007.07367v1","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07367","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_12","alias_value":"EOWIRCVRHPNW","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_16","alias_value":"EOWIRCVRHPNWSOQ2","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_8","alias_value":"EOWIRCVR","created_at":"2026-07-05T01:19:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:EOWIRCVRHPNWSOQ2BOCJRUGER4","target":"record","payload":{"canonical_record":{"source":{"id":"2007.07367","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-14T21:25:39Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ce10156c689430a2e40a16622a295974a9a9404c8f830cef36d11e3a4478fa42","abstract_canon_sha256":"855d180f2c599fb3177095e5dde72a24e4fc7b98334d2d8c56088acdba0706d8"},"schema_version":"1.0"},"canonical_sha256":"23ac888ab13bdb693a1a0b8498d0c48f105c06feb03102707b52836836cc300b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:19:16.453507Z","signature_b64":"pjzBNVqeDk+j+jruj0Ay0gSrjYEgDF5LsHW8s3jzBxdWKnFs8StfL0iA2ECgxXKQr3OfgTipj+cQrn4u4jzQBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"23ac888ab13bdb693a1a0b8498d0c48f105c06feb03102707b52836836cc300b","last_reissued_at":"2026-07-05T01:19:16.453057Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:19:16.453057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.07367","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":"2026-07-05T01:19:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KEsGkjrzrOw8JI6DUQbZy8//Au5l+SpZbdw8kiSdBiCFNrKJRMIzdyI3gZQje+q/YA3L+WZuDopq+H40k6BhCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:16:44.842582Z"},"content_sha256":"a1f924b8be5490850dd36cd7fd79789c5f998d8ddbf33ec50f24bf68e3709512","schema_version":"1.0","event_id":"sha256:a1f924b8be5490850dd36cd7fd79789c5f998d8ddbf33ec50f24bf68e3709512"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:EOWIRCVRHPNWSOQ2BOCJRUGER4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Streaming Probabilistic Deep Tensor Factorization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ji Liu, Shandian Zhe, Shikai Fang, Zheng Wang, Zhimeng Pan","submitted_at":"2020-07-14T21:25:39Z","abstract_excerpt":"Despite the success of existing tensor factorization methods, most of them conduct a multilinear decomposition, and rarely exploit powerful modeling frameworks, like deep neural networks, to capture a variety of complicated interactions in data. More important, for highly expressive, deep factorization, we lack an effective approach to handle streaming data, which are ubiquitous in real-world applications. To address these issues, we propose SPIDER, a Streaming ProbabilistIc Deep tEnsoR factorization method. We first use Bayesian neural networks (NNs) to construct a deep tensor factorization m"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07367","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/2007.07367/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":"2026-07-05T01:19:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gm2ojVFqJWTYH9Kcw9WkQc6MPNfkIJbs+mFx1LwKwqnhWhS2hgiSmDpMuZwHsi0+PjtK/RRPr5jzphwN5gvbCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T17:16:44.842956Z"},"content_sha256":"f874e42a16e85d2ed14df4d0e602df2d947543a412375e661da06f289b8e0241","schema_version":"1.0","event_id":"sha256:f874e42a16e85d2ed14df4d0e602df2d947543a412375e661da06f289b8e0241"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/bundle.json","state_url":"https://pith.science/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/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":"2026-07-08T17:16:44Z","links":{"resolver":"https://pith.science/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4","bundle":"https://pith.science/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/bundle.json","state":"https://pith.science/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EOWIRCVRHPNWSOQ2BOCJRUGER4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:EOWIRCVRHPNWSOQ2BOCJRUGER4","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":"855d180f2c599fb3177095e5dde72a24e4fc7b98334d2d8c56088acdba0706d8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-14T21:25:39Z","title_canon_sha256":"ce10156c689430a2e40a16622a295974a9a9404c8f830cef36d11e3a4478fa42"},"schema_version":"1.0","source":{"id":"2007.07367","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.07367","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"arxiv_version","alias_value":"2007.07367v1","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07367","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_12","alias_value":"EOWIRCVRHPNW","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_16","alias_value":"EOWIRCVRHPNWSOQ2","created_at":"2026-07-05T01:19:16Z"},{"alias_kind":"pith_short_8","alias_value":"EOWIRCVR","created_at":"2026-07-05T01:19:16Z"}],"graph_snapshots":[{"event_id":"sha256:f874e42a16e85d2ed14df4d0e602df2d947543a412375e661da06f289b8e0241","target":"graph","created_at":"2026-07-05T01:19:16Z","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/2007.07367/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite the success of existing tensor factorization methods, most of them conduct a multilinear decomposition, and rarely exploit powerful modeling frameworks, like deep neural networks, to capture a variety of complicated interactions in data. More important, for highly expressive, deep factorization, we lack an effective approach to handle streaming data, which are ubiquitous in real-world applications. To address these issues, we propose SPIDER, a Streaming ProbabilistIc Deep tEnsoR factorization method. We first use Bayesian neural networks (NNs) to construct a deep tensor factorization m","authors_text":"Ji Liu, Shandian Zhe, Shikai Fang, Zheng Wang, Zhimeng Pan","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-14T21:25:39Z","title":"Streaming Probabilistic Deep Tensor Factorization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07367","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:a1f924b8be5490850dd36cd7fd79789c5f998d8ddbf33ec50f24bf68e3709512","target":"record","created_at":"2026-07-05T01:19:16Z","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":"855d180f2c599fb3177095e5dde72a24e4fc7b98334d2d8c56088acdba0706d8","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-07-14T21:25:39Z","title_canon_sha256":"ce10156c689430a2e40a16622a295974a9a9404c8f830cef36d11e3a4478fa42"},"schema_version":"1.0","source":{"id":"2007.07367","kind":"arxiv","version":1}},"canonical_sha256":"23ac888ab13bdb693a1a0b8498d0c48f105c06feb03102707b52836836cc300b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"23ac888ab13bdb693a1a0b8498d0c48f105c06feb03102707b52836836cc300b","first_computed_at":"2026-07-05T01:19:16.453057Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:19:16.453057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pjzBNVqeDk+j+jruj0Ay0gSrjYEgDF5LsHW8s3jzBxdWKnFs8StfL0iA2ECgxXKQr3OfgTipj+cQrn4u4jzQBg==","signature_status":"signed_v1","signed_at":"2026-07-05T01:19:16.453507Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.07367","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a1f924b8be5490850dd36cd7fd79789c5f998d8ddbf33ec50f24bf68e3709512","sha256:f874e42a16e85d2ed14df4d0e602df2d947543a412375e661da06f289b8e0241"],"state_sha256":"c8fd1cf99d8b7a7724697bed0811a23bdab55f51334894c9007c4231a00523c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"i+1q8tLKkiYQD7U9c6Qp9MiLPLT4hE+xBL2v0xjI5i7wPLkQVLR4karkr+in8rX5vLVd0RScBW5oo51wAN0+AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T17:16:44.844959Z","bundle_sha256":"12649378275fc8468616c40aefd3ddcb6a7f3624866eb06bde77b3fb5494ce8d"}}