{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:4D5I74FJUBLJI4Q4YSRJ2VRQMC","short_pith_number":"pith:4D5I74FJ","schema_version":"1.0","canonical_sha256":"e0fa8ff0a9a05694721cc4a29d563060bdc715903c6c9188339b14568aab897a","source":{"kind":"arxiv","id":"2212.13554","version":2},"attestation_state":"computed","paper":{"title":"NeRN -- Learning Neural Representations for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Elad Richardson, Eran Treister, Maor Ashkenazi, Pinchas Mintz, Ron Vainshtein, Shir Levi, Zohar Rimon","submitted_at":"2022-12-27T17:14:44Z","abstract_excerpt":"Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates"},"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":"2212.13554","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2022-12-27T17:14:44Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"2bf364ca3bb6c981e4407548fadbe8906e32de38cbce565fbc16f9cbd10db4c0","abstract_canon_sha256":"3a61bc88d4163b0747689bb42bc4eb3b1bec8753c37ec8f1997645e00331f935"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:03:11.000218Z","signature_b64":"HAtvP2qkHgzEHDBUHUtpUb6D9VNOMRLkrWuHockZRCh2oGxfbVfVbR6+NvieeXuPvtqSpTojMqByd3RtZTPGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e0fa8ff0a9a05694721cc4a29d563060bdc715903c6c9188339b14568aab897a","last_reissued_at":"2026-07-05T06:03:10.999730Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:03:10.999730Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NeRN -- Learning Neural Representations for Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Elad Richardson, Eran Treister, Maor Ashkenazi, Pinchas Mintz, Ron Vainshtein, Shir Levi, Zohar Rimon","submitted_at":"2022-12-27T17:14:44Z","abstract_excerpt":"Neural Representations have recently been shown to effectively reconstruct a wide range of signals from 3D meshes and shapes to images and videos. We show that, when adapted correctly, neural representations can be used to directly represent the weights of a pre-trained convolutional neural network, resulting in a Neural Representation for Neural Networks (NeRN). Inspired by coordinate inputs of previous neural representation methods, we assign a coordinate to each convolutional kernel in our network based on its position in the architecture, and optimize a predictor network to map coordinates"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.13554","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.13554/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":"2212.13554","created_at":"2026-07-05T06:03:10.999790+00:00"},{"alias_kind":"arxiv_version","alias_value":"2212.13554v2","created_at":"2026-07-05T06:03:10.999790+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.13554","created_at":"2026-07-05T06:03:10.999790+00:00"},{"alias_kind":"pith_short_12","alias_value":"4D5I74FJUBLJ","created_at":"2026-07-05T06:03:10.999790+00:00"},{"alias_kind":"pith_short_16","alias_value":"4D5I74FJUBLJI4Q4","created_at":"2026-07-05T06:03:10.999790+00:00"},{"alias_kind":"pith_short_8","alias_value":"4D5I74FJ","created_at":"2026-07-05T06:03:10.999790+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2502.09619","citing_title":"Can this Model Also Recognize Dogs? Zero-Shot Model Search from Weights","ref_index":1,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC","json":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC.json","graph_json":"https://pith.science/api/pith-number/4D5I74FJUBLJI4Q4YSRJ2VRQMC/graph.json","events_json":"https://pith.science/api/pith-number/4D5I74FJUBLJI4Q4YSRJ2VRQMC/events.json","paper":"https://pith.science/paper/4D5I74FJ"},"agent_actions":{"view_html":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC","download_json":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC.json","view_paper":"https://pith.science/paper/4D5I74FJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2212.13554&json=true","fetch_graph":"https://pith.science/api/pith-number/4D5I74FJUBLJI4Q4YSRJ2VRQMC/graph.json","fetch_events":"https://pith.science/api/pith-number/4D5I74FJUBLJI4Q4YSRJ2VRQMC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC/action/storage_attestation","attest_author":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC/action/author_attestation","sign_citation":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC/action/citation_signature","submit_replication":"https://pith.science/pith/4D5I74FJUBLJI4Q4YSRJ2VRQMC/action/replication_record"}},"created_at":"2026-07-05T06:03:10.999790+00:00","updated_at":"2026-07-05T06:03:10.999790+00:00"}