{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:T5HEJN43HWH6NNM2KOPWNONYLF","short_pith_number":"pith:T5HEJN43","canonical_record":{"source":{"id":"2407.00356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-29T08:07:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"72736856d0be95e3f739b43f035ee58a334901d8143209a967e1059b6de5c5f2","abstract_canon_sha256":"4a6d890b7de9b6ada045940fdb91c2f2c19f5c48bbb5a141b8f3b8b7b749a01d"},"schema_version":"1.0"},"canonical_sha256":"9f4e44b79b3d8fe6b59a539f66b9b85943f5ed26951dcd48cb79acfb7aa2dfaf","source":{"kind":"arxiv","id":"2407.00356","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.00356","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.00356v1","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00356","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_12","alias_value":"T5HEJN43HWH6","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_16","alias_value":"T5HEJN43HWH6NNM2","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_8","alias_value":"T5HEJN43","created_at":"2026-07-05T08:38:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:T5HEJN43HWH6NNM2KOPWNONYLF","target":"record","payload":{"canonical_record":{"source":{"id":"2407.00356","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-29T08:07:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"72736856d0be95e3f739b43f035ee58a334901d8143209a967e1059b6de5c5f2","abstract_canon_sha256":"4a6d890b7de9b6ada045940fdb91c2f2c19f5c48bbb5a141b8f3b8b7b749a01d"},"schema_version":"1.0"},"canonical_sha256":"9f4e44b79b3d8fe6b59a539f66b9b85943f5ed26951dcd48cb79acfb7aa2dfaf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:38:18.607602Z","signature_b64":"UnbXZu7l24BSiRiZLcICnVnkG92kmE73XPW3k9nlw9EeULa0Jrjy183cj8G8MYih2hBLSBHEEtNptoEoe2PPAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f4e44b79b3d8fe6b59a539f66b9b85943f5ed26951dcd48cb79acfb7aa2dfaf","last_reissued_at":"2026-07-05T08:38:18.607126Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:38:18.607126Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2407.00356","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":"2026-07-05T08:38:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"D3KETHuryUq7IPHb/NrH1fQCqgDIIKUn98GbE5ELEf7Fg/OwyK66GZrPNJVpg3lheRSKVkVPfoKXILomSDMlDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:31:23.943298Z"},"content_sha256":"d734b820eb1a753bcc3392da98a3ab8dd95b663211c375f1c642a9f3826abcdb","schema_version":"1.0","event_id":"sha256:d734b820eb1a753bcc3392da98a3ab8dd95b663211c375f1c642a9f3826abcdb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:T5HEJN43HWH6NNM2KOPWNONYLF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu","submitted_at":"2024-06-29T08:07:39Z","abstract_excerpt":"In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model accuracy is the sole objective, it can be achieved effectively through the weight reconstruction objective alone. Additionally, we explore the underlying factors for improving weight reconstruction under parameter-efficiency constraints, and propose a novel training scheme that decouples the reconstruction objective from auxiliary objectives such as "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00356","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/2407.00356/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":"2026-07-05T08:38:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T9eCHHwcZ1wlnlFwhJImQ2mI8ijNYbP1CYQJCHoO4wX+vnxEROy0N51bq5+J0omELELoOBlRda/LFPt131O8Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:31:23.943696Z"},"content_sha256":"3bd56b219e7df993e1107b9a08f9308148c70930a6c66b2d5c2106ee5c2adbfb","schema_version":"1.0","event_id":"sha256:3bd56b219e7df993e1107b9a08f9308148c70930a6c66b2d5c2106ee5c2adbfb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T5HEJN43HWH6NNM2KOPWNONYLF/bundle.json","state_url":"https://pith.science/pith/T5HEJN43HWH6NNM2KOPWNONYLF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T5HEJN43HWH6NNM2KOPWNONYLF/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":"2026-07-07T11:31:23Z","links":{"resolver":"https://pith.science/pith/T5HEJN43HWH6NNM2KOPWNONYLF","bundle":"https://pith.science/pith/T5HEJN43HWH6NNM2KOPWNONYLF/bundle.json","state":"https://pith.science/pith/T5HEJN43HWH6NNM2KOPWNONYLF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T5HEJN43HWH6NNM2KOPWNONYLF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:T5HEJN43HWH6NNM2KOPWNONYLF","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":"4a6d890b7de9b6ada045940fdb91c2f2c19f5c48bbb5a141b8f3b8b7b749a01d","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-29T08:07:39Z","title_canon_sha256":"72736856d0be95e3f739b43f035ee58a334901d8143209a967e1059b6de5c5f2"},"schema_version":"1.0","source":{"id":"2407.00356","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2407.00356","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"arxiv_version","alias_value":"2407.00356v1","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.00356","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_12","alias_value":"T5HEJN43HWH6","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_16","alias_value":"T5HEJN43HWH6NNM2","created_at":"2026-07-05T08:38:18Z"},{"alias_kind":"pith_short_8","alias_value":"T5HEJN43","created_at":"2026-07-05T08:38:18Z"}],"graph_snapshots":[{"event_id":"sha256:3bd56b219e7df993e1107b9a08f9308148c70930a6c66b2d5c2106ee5c2adbfb","target":"graph","created_at":"2026-07-05T08:38:18Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2407.00356/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, we investigate the fundamental trade-off regarding accuracy and parameter efficiency in the parameterization of neural network weights using predictor networks. We present a surprising finding that, when recovering the original model accuracy is the sole objective, it can be achieved effectively through the weight reconstruction objective alone. Additionally, we explore the underlying factors for improving weight reconstruction under parameter-efficiency constraints, and propose a novel training scheme that decouples the reconstruction objective from auxiliary objectives such as ","authors_text":"Hongjun Choi, Jayaraman J. Thiagarajan, Ruben Glatt, Shusen Liu","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-29T08:07:39Z","title":"Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.00356","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:d734b820eb1a753bcc3392da98a3ab8dd95b663211c375f1c642a9f3826abcdb","target":"record","created_at":"2026-07-05T08:38:18Z","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":"4a6d890b7de9b6ada045940fdb91c2f2c19f5c48bbb5a141b8f3b8b7b749a01d","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-06-29T08:07:39Z","title_canon_sha256":"72736856d0be95e3f739b43f035ee58a334901d8143209a967e1059b6de5c5f2"},"schema_version":"1.0","source":{"id":"2407.00356","kind":"arxiv","version":1}},"canonical_sha256":"9f4e44b79b3d8fe6b59a539f66b9b85943f5ed26951dcd48cb79acfb7aa2dfaf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9f4e44b79b3d8fe6b59a539f66b9b85943f5ed26951dcd48cb79acfb7aa2dfaf","first_computed_at":"2026-07-05T08:38:18.607126Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:38:18.607126Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UnbXZu7l24BSiRiZLcICnVnkG92kmE73XPW3k9nlw9EeULa0Jrjy183cj8G8MYih2hBLSBHEEtNptoEoe2PPAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:38:18.607602Z","signed_message":"canonical_sha256_bytes"},"source_id":"2407.00356","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d734b820eb1a753bcc3392da98a3ab8dd95b663211c375f1c642a9f3826abcdb","sha256:3bd56b219e7df993e1107b9a08f9308148c70930a6c66b2d5c2106ee5c2adbfb"],"state_sha256":"3e8d242482a3ed02cf9b3774a42ed9a02ea0df7e16a104467b38585ae2a1fed7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/xr0/FNTpzMk88NzaX91E4uo8ewWE9v76uoXOLp5TkrnXxxxaa+t2ZDCFC2WNviYmDwQos1EoyRnhuFtsTEcCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:31:23.945701Z","bundle_sha256":"5df81b7cac8d54279fb0ec93f34feb081d46838faaed27e99e6253833ce8a2a9"}}