{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:DHUWSM6BIRCCMP6UL2SQFHJLU4","short_pith_number":"pith:DHUWSM6B","canonical_record":{"source":{"id":"2301.08883","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-21T04:08:46Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"4126542dc6db19c1f33e4437eb768a49ef37164bcaf62aa67df942b70273f0a4","abstract_canon_sha256":"d37d3e21ea325ab57a131139fb75abad130070e04dbe3a3dfb765a149a5ed7e5"},"schema_version":"1.0"},"canonical_sha256":"19e96933c14444263fd45ea5029d2ba70570ac9e6aa907c29467e4cfc1e22082","source":{"kind":"arxiv","id":"2301.08883","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.08883","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"arxiv_version","alias_value":"2301.08883v3","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.08883","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_12","alias_value":"DHUWSM6BIRCC","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_16","alias_value":"DHUWSM6BIRCCMP6U","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_8","alias_value":"DHUWSM6B","created_at":"2026-07-05T05:44:13Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:DHUWSM6BIRCCMP6UL2SQFHJLU4","target":"record","payload":{"canonical_record":{"source":{"id":"2301.08883","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-21T04:08:46Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"4126542dc6db19c1f33e4437eb768a49ef37164bcaf62aa67df942b70273f0a4","abstract_canon_sha256":"d37d3e21ea325ab57a131139fb75abad130070e04dbe3a3dfb765a149a5ed7e5"},"schema_version":"1.0"},"canonical_sha256":"19e96933c14444263fd45ea5029d2ba70570ac9e6aa907c29467e4cfc1e22082","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:44:13.290994Z","signature_b64":"/n0iStcaXgnVEGShMuy+KdW4zHzXubXxc6CIF9IXsaf2X6EC4Zecri/8a+f8P3Fo8Urq+Medhg+0StXHNcH9Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19e96933c14444263fd45ea5029d2ba70570ac9e6aa907c29467e4cfc1e22082","last_reissued_at":"2026-07-05T05:44:13.290465Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:44:13.290465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2301.08883","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:44:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DNWnVbJsaALmTt84ieRkBLYBkBQY1d6gQvN0ipVcZiPW2W83UGXKFep/XcYAG7/ct2SAO2YtvnLeizKPNzLCCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:09:04.082567Z"},"content_sha256":"c94c63c20c7c6dd903ccd63c665b2dc80007bf9ac3af5ec580f8264b964334f2","schema_version":"1.0","event_id":"sha256:c94c63c20c7c6dd903ccd63c665b2dc80007bf9ac3af5ec580f8264b964334f2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:DHUWSM6BIRCCMP6UL2SQFHJLU4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Versatile Neural Processes for Learning Implicit Neural Representations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"cs.LG","authors_text":"Cuiling Lan, Yan Lu, Zhibo Chen, Zhizheng Zhang, Zongyu Guo","submitted_at":"2023-01-21T04:08:46Z","abstract_excerpt":"Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical solution for fast inference of continuous functions. However, existing NP architectures suffer from inferior modeling capability for complex signals. In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.08883","kind":"arxiv","version":3},"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/2301.08883/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":"2026-07-05T05:44:13Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zaTH2t0Yc3NVP9ECkgYlwVkwOsrsCpTJcdI2vz3fFau6ergiOPECfuS2xouxlVlUX0jsBcp70J6Op4EUMcwZCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T06:09:04.083095Z"},"content_sha256":"7537104da26dcc89c851ab29a367b92bf7d3858ca0bce010b2f3e26d4470fc80","schema_version":"1.0","event_id":"sha256:7537104da26dcc89c851ab29a367b92bf7d3858ca0bce010b2f3e26d4470fc80"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/bundle.json","state_url":"https://pith.science/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/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":"2026-08-10T06:09:04Z","links":{"resolver":"https://pith.science/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4","bundle":"https://pith.science/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/bundle.json","state":"https://pith.science/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DHUWSM6BIRCCMP6UL2SQFHJLU4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:DHUWSM6BIRCCMP6UL2SQFHJLU4","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":"d37d3e21ea325ab57a131139fb75abad130070e04dbe3a3dfb765a149a5ed7e5","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-21T04:08:46Z","title_canon_sha256":"4126542dc6db19c1f33e4437eb768a49ef37164bcaf62aa67df942b70273f0a4"},"schema_version":"1.0","source":{"id":"2301.08883","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2301.08883","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"arxiv_version","alias_value":"2301.08883v3","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.08883","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_12","alias_value":"DHUWSM6BIRCC","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_16","alias_value":"DHUWSM6BIRCCMP6U","created_at":"2026-07-05T05:44:13Z"},{"alias_kind":"pith_short_8","alias_value":"DHUWSM6B","created_at":"2026-07-05T05:44:13Z"}],"graph_snapshots":[{"event_id":"sha256:7537104da26dcc89c851ab29a367b92bf7d3858ca0bce010b2f3e26d4470fc80","target":"graph","created_at":"2026-07-05T05:44:13Z","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/2301.08883/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Representing a signal as a continuous function parameterized by neural network (a.k.a. Implicit Neural Representations, INRs) has attracted increasing attention in recent years. Neural Processes (NPs), which model the distributions over functions conditioned on partial observations (context set), provide a practical solution for fast inference of continuous functions. However, existing NP architectures suffer from inferior modeling capability for complex signals. In this paper, we propose an efficient NP framework dubbed Versatile Neural Processes (VNP), which largely increases the capability ","authors_text":"Cuiling Lan, Yan Lu, Zhibo Chen, Zhizheng Zhang, Zongyu Guo","cross_cats":["eess.SP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-21T04:08:46Z","title":"Versatile Neural Processes for Learning Implicit Neural Representations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.08883","kind":"arxiv","version":3},"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:c94c63c20c7c6dd903ccd63c665b2dc80007bf9ac3af5ec580f8264b964334f2","target":"record","created_at":"2026-07-05T05:44:13Z","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":"d37d3e21ea325ab57a131139fb75abad130070e04dbe3a3dfb765a149a5ed7e5","cross_cats_sorted":["eess.SP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-21T04:08:46Z","title_canon_sha256":"4126542dc6db19c1f33e4437eb768a49ef37164bcaf62aa67df942b70273f0a4"},"schema_version":"1.0","source":{"id":"2301.08883","kind":"arxiv","version":3}},"canonical_sha256":"19e96933c14444263fd45ea5029d2ba70570ac9e6aa907c29467e4cfc1e22082","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"19e96933c14444263fd45ea5029d2ba70570ac9e6aa907c29467e4cfc1e22082","first_computed_at":"2026-07-05T05:44:13.290465Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:44:13.290465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/n0iStcaXgnVEGShMuy+KdW4zHzXubXxc6CIF9IXsaf2X6EC4Zecri/8a+f8P3Fo8Urq+Medhg+0StXHNcH9Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:44:13.290994Z","signed_message":"canonical_sha256_bytes"},"source_id":"2301.08883","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c94c63c20c7c6dd903ccd63c665b2dc80007bf9ac3af5ec580f8264b964334f2","sha256:7537104da26dcc89c851ab29a367b92bf7d3858ca0bce010b2f3e26d4470fc80"],"state_sha256":"10f2b342bebb8064176bc68d283cf1134ff8db8a129f909b583e107ee7075fc7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+bgr73CaGDfzzehl+p6itTbYbTdMcC5z5NJY7fdRd3zpXrziysBjvd6vK1V0YD52hhcRjQvIkL2zivbfriOCBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T06:09:04.086318Z","bundle_sha256":"67a493caa9a514f083f9dd25a01ebae5724d6d383cc9cbf5307d1660f070f980"}}