{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:RVIN3H44UXQSARKRCGXBDVVE5X","short_pith_number":"pith:RVIN3H44","canonical_record":{"source":{"id":"1802.06825","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T19:54:46Z","cross_cats_sorted":[],"title_canon_sha256":"342ae63a5f10fa83d3be3a895e0be81758250d49ee2c277f9ff22459c864f99e","abstract_canon_sha256":"28ff377de339f53e9725667040c0cbf727a4488991abf63ebac49e1e46f55d2e"},"schema_version":"1.0"},"canonical_sha256":"8d50dd9f9ca5e120455111ae11d6a4edf73bf36e4d62d4264a37b14011cf03e5","source":{"kind":"arxiv","id":"1802.06825","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06825","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06825v2","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06825","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"RVIN3H44UXQS","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RVIN3H44UXQSARKR","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RVIN3H44","created_at":"2026-05-18T12:32:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:RVIN3H44UXQSARKRCGXBDVVE5X","target":"record","payload":{"canonical_record":{"source":{"id":"1802.06825","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T19:54:46Z","cross_cats_sorted":[],"title_canon_sha256":"342ae63a5f10fa83d3be3a895e0be81758250d49ee2c277f9ff22459c864f99e","abstract_canon_sha256":"28ff377de339f53e9725667040c0cbf727a4488991abf63ebac49e1e46f55d2e"},"schema_version":"1.0"},"canonical_sha256":"8d50dd9f9ca5e120455111ae11d6a4edf73bf36e4d62d4264a37b14011cf03e5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:18.278365Z","signature_b64":"9O9ZlLOQ4Z+2zHZQwc9OyQ4aUaJTzHA4VMa9yWoRuL6ENFPNU+3aWzvMeDhCSuTNhJ5JXgPeshFUuR77UUBTCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8d50dd9f9ca5e120455111ae11d6a4edf73bf36e4d62d4264a37b14011cf03e5","last_reissued_at":"2026-05-18T00:22:18.277853Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:18.277853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.06825","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IEAef9v90c2RYe4IO9cBOg1980QnhYnpnDEok0aIRQCufyZBnhb8BK+eZrtzCieTgCqviCpZWZ7bvKtEHi5nAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:13:33.118993Z"},"content_sha256":"3fde87ba5b81a38198e81e393f888b14f8fabef5c8a77215a6d1e2f31bce3099","schema_version":"1.0","event_id":"sha256:3fde87ba5b81a38198e81e393f888b14f8fabef5c8a77215a6d1e2f31bce3099"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:RVIN3H44UXQSARKRCGXBDVVE5X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-resolution Tensor Learning for Large-Scale Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Rose Yu, Stephan Zheng, Yisong Yue","submitted_at":"2018-02-19T19:54:46Z","abstract_excerpt":"High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In order to not \"over-train\" on coarse resolution models, we investigate an information-theoretic fine-gr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06825","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":""},"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":"2026-05-18T00:22:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9yzSCjC4pZFb+osrP0EvdVGforZtYAuhLTuRJbZo7esWjq8d2qbo8skDQYklRXHGeOnlIgNgpNfUcCbvn4agDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T19:13:33.119348Z"},"content_sha256":"b20917b148d03c2a72958fc27b962ea337a32c79f292107a90f41ae1951b9a51","schema_version":"1.0","event_id":"sha256:b20917b148d03c2a72958fc27b962ea337a32c79f292107a90f41ae1951b9a51"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/RVIN3H44UXQSARKRCGXBDVVE5X/bundle.json","state_url":"https://pith.science/pith/RVIN3H44UXQSARKRCGXBDVVE5X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/RVIN3H44UXQSARKRCGXBDVVE5X/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":"2026-06-01T19:13:33Z","links":{"resolver":"https://pith.science/pith/RVIN3H44UXQSARKRCGXBDVVE5X","bundle":"https://pith.science/pith/RVIN3H44UXQSARKRCGXBDVVE5X/bundle.json","state":"https://pith.science/pith/RVIN3H44UXQSARKRCGXBDVVE5X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/RVIN3H44UXQSARKRCGXBDVVE5X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:RVIN3H44UXQSARKRCGXBDVVE5X","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":"28ff377de339f53e9725667040c0cbf727a4488991abf63ebac49e1e46f55d2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T19:54:46Z","title_canon_sha256":"342ae63a5f10fa83d3be3a895e0be81758250d49ee2c277f9ff22459c864f99e"},"schema_version":"1.0","source":{"id":"1802.06825","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.06825","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"arxiv_version","alias_value":"1802.06825v2","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.06825","created_at":"2026-05-18T00:22:18Z"},{"alias_kind":"pith_short_12","alias_value":"RVIN3H44UXQS","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_16","alias_value":"RVIN3H44UXQSARKR","created_at":"2026-05-18T12:32:50Z"},{"alias_kind":"pith_short_8","alias_value":"RVIN3H44","created_at":"2026-05-18T12:32:50Z"}],"graph_snapshots":[{"event_id":"sha256:b20917b148d03c2a72958fc27b962ea337a32c79f292107a90f41ae1951b9a51","target":"graph","created_at":"2026-05-18T00:22:18Z","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"},"paper":{"abstract_excerpt":"High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models. MMT leverages the property that spatial data can be viewed at multiple resolutions, which are related by coarsening and finegraining from one resolution to another. Using this property, MMT learns a tensor model by starting from a coarse resolution and iteratively increasing the model complexity. In order to not \"over-train\" on coarse resolution models, we investigate an information-theoretic fine-gr","authors_text":"Rose Yu, Stephan Zheng, Yisong Yue","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T19:54:46Z","title":"Multi-resolution Tensor Learning for Large-Scale Spatial Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.06825","kind":"arxiv","version":2},"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":"sha256:3fde87ba5b81a38198e81e393f888b14f8fabef5c8a77215a6d1e2f31bce3099","target":"record","created_at":"2026-05-18T00:22:18Z","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":"28ff377de339f53e9725667040c0cbf727a4488991abf63ebac49e1e46f55d2e","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-02-19T19:54:46Z","title_canon_sha256":"342ae63a5f10fa83d3be3a895e0be81758250d49ee2c277f9ff22459c864f99e"},"schema_version":"1.0","source":{"id":"1802.06825","kind":"arxiv","version":2}},"canonical_sha256":"8d50dd9f9ca5e120455111ae11d6a4edf73bf36e4d62d4264a37b14011cf03e5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d50dd9f9ca5e120455111ae11d6a4edf73bf36e4d62d4264a37b14011cf03e5","first_computed_at":"2026-05-18T00:22:18.277853Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:18.277853Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9O9ZlLOQ4Z+2zHZQwc9OyQ4aUaJTzHA4VMa9yWoRuL6ENFPNU+3aWzvMeDhCSuTNhJ5JXgPeshFUuR77UUBTCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:18.278365Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.06825","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3fde87ba5b81a38198e81e393f888b14f8fabef5c8a77215a6d1e2f31bce3099","sha256:b20917b148d03c2a72958fc27b962ea337a32c79f292107a90f41ae1951b9a51"],"state_sha256":"4492d5f45fc94c1c0da18376ef425f690cfb34802c618920426a04a452742a1a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OD6CgFix3LthmBxXsk5EEY41X4g8cpKBwiuLWAVbsxrloAvnGNqNDd4xBqITIpiD0uQ/rEHfb5cj31SQWs02CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T19:13:33.121281Z","bundle_sha256":"0e5489726c3285204b465a60d2c8190b596ddd34fbeeb3e23c8d07eb1b2b429d"}}