{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:2KH3OR577AHHXEDVFE3IJFOROI","short_pith_number":"pith:2KH3OR57","schema_version":"1.0","canonical_sha256":"d28fb747bff80e7b907529368495d1723ba4cf35182ded462511e7d80bb2ab13","source":{"kind":"arxiv","id":"2211.13006","version":4},"attestation_state":"computed","paper":{"title":"Quantized Compressed Sensing with Score-based Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Xiangming Meng, Yoshiyuki Kabashima","submitted_at":"2022-11-02T15:19:07Z","abstract_excerpt":"We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measurements, leads to severe information loss and thus a good prior knowledge of the unknown signal is helpful for accurate recovery. Motivated by the power of score-based generative models (SGM, also known as diffusion models) in capturing the rich structure of natural signals beyond simple sparsity, we propose an unsupervised data-driven approach called quantized compressed sensing with SGM (QCS-SGM), where the prior distr"},"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":"2211.13006","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2022-11-02T15:19:07Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"b2a0012f7aed28661af9956fede1b216aef8f2b4d94b80ef5955c23a7864370d","abstract_canon_sha256":"e6dfee23a91c742af29f986b1208834e8321f607db3e1331c93126c77a18855c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:43:17.711062Z","signature_b64":"b1WwUG9SpekSq0Z/EDnv7PUeluMmKm4FObYDNw3NPWWUY/hMVz6x3ZukMHIQKOT2M7yFRYLCUCqPDTikTxDMBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d28fb747bff80e7b907529368495d1723ba4cf35182ded462511e7d80bb2ab13","last_reissued_at":"2026-07-05T05:43:17.710587Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:43:17.710587Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Quantized Compressed Sensing with Score-based Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"eess.SP","authors_text":"Xiangming Meng, Yoshiyuki Kabashima","submitted_at":"2022-11-02T15:19:07Z","abstract_excerpt":"We consider the general problem of recovering a high-dimensional signal from noisy quantized measurements. Quantization, especially coarse quantization such as 1-bit sign measurements, leads to severe information loss and thus a good prior knowledge of the unknown signal is helpful for accurate recovery. Motivated by the power of score-based generative models (SGM, also known as diffusion models) in capturing the rich structure of natural signals beyond simple sparsity, we propose an unsupervised data-driven approach called quantized compressed sensing with SGM (QCS-SGM), where the prior distr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2211.13006","kind":"arxiv","version":4},"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/2211.13006/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":"2211.13006","created_at":"2026-07-05T05:43:17.710659+00:00"},{"alias_kind":"arxiv_version","alias_value":"2211.13006v4","created_at":"2026-07-05T05:43:17.710659+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2211.13006","created_at":"2026-07-05T05:43:17.710659+00:00"},{"alias_kind":"pith_short_12","alias_value":"2KH3OR577AHH","created_at":"2026-07-05T05:43:17.710659+00:00"},{"alias_kind":"pith_short_16","alias_value":"2KH3OR577AHHXEDV","created_at":"2026-07-05T05:43:17.710659+00:00"},{"alias_kind":"pith_short_8","alias_value":"2KH3OR57","created_at":"2026-07-05T05:43:17.710659+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.21135","citing_title":"Learning Single Index Models with Diffusion Priors","ref_index":68,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI","json":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI.json","graph_json":"https://pith.science/api/pith-number/2KH3OR577AHHXEDVFE3IJFOROI/graph.json","events_json":"https://pith.science/api/pith-number/2KH3OR577AHHXEDVFE3IJFOROI/events.json","paper":"https://pith.science/paper/2KH3OR57"},"agent_actions":{"view_html":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI","download_json":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI.json","view_paper":"https://pith.science/paper/2KH3OR57","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2211.13006&json=true","fetch_graph":"https://pith.science/api/pith-number/2KH3OR577AHHXEDVFE3IJFOROI/graph.json","fetch_events":"https://pith.science/api/pith-number/2KH3OR577AHHXEDVFE3IJFOROI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI/action/storage_attestation","attest_author":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI/action/author_attestation","sign_citation":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI/action/citation_signature","submit_replication":"https://pith.science/pith/2KH3OR577AHHXEDVFE3IJFOROI/action/replication_record"}},"created_at":"2026-07-05T05:43:17.710659+00:00","updated_at":"2026-07-05T05:43:17.710659+00:00"}