{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:VDA3PNLBO6CZL23T2ZRJRF74IF","short_pith_number":"pith:VDA3PNLB","canonical_record":{"source":{"id":"2003.08334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-18T17:02:10Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"6c6166c35f087a5325bb87915f30ad0d3ec9483de58fb7a785cd733f4f6c1e06","abstract_canon_sha256":"09e9cb1595a6c62b56d9e2e1ab4e14fd62987a24316d0a12fcb1c07270f1c6e2"},"schema_version":"1.0"},"canonical_sha256":"a8c1b7b561778595eb73d6629897fc4178dff43d97850fa8a4da1f9a77558379","source":{"kind":"arxiv","id":"2003.08334","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08334","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08334v1","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08334","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"VDA3PNLBO6CZ","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_16","alias_value":"VDA3PNLBO6CZL23T","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_8","alias_value":"VDA3PNLB","created_at":"2026-07-05T00:49:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:VDA3PNLBO6CZL23T2ZRJRF74IF","target":"record","payload":{"canonical_record":{"source":{"id":"2003.08334","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-18T17:02:10Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"6c6166c35f087a5325bb87915f30ad0d3ec9483de58fb7a785cd733f4f6c1e06","abstract_canon_sha256":"09e9cb1595a6c62b56d9e2e1ab4e14fd62987a24316d0a12fcb1c07270f1c6e2"},"schema_version":"1.0"},"canonical_sha256":"a8c1b7b561778595eb73d6629897fc4178dff43d97850fa8a4da1f9a77558379","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:49:06.115821Z","signature_b64":"ZFO9rKkb4RSB9ud+6nohDfoOM/Vb3SbvqMhBxp3eQXzlb1pGh+WkX0/eIsd51Jq91B/cegi1oZXcP6breLlxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a8c1b7b561778595eb73d6629897fc4178dff43d97850fa8a4da1f9a77558379","last_reissued_at":"2026-07-05T00:49:06.115403Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:49:06.115403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2003.08334","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":"2026-07-05T00:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AC/B0wOkxruNpxCn3aD+Pxw/0tiDmq40NkcC6y+iyH897dIrPCeAMRf1tfiCOPHU/hjiq1UsceIDarDQlKOAAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:28:59.659493Z"},"content_sha256":"38789fe09cff42d05fe106d884faf8a12dcc1402d8b4aa8735083f48379c4e53","schema_version":"1.0","event_id":"sha256:38789fe09cff42d05fe106d884faf8a12dcc1402d8b4aa8735083f48379c4e53"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:VDA3PNLBO6CZL23T2ZRJRF74IF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\\ell_1$-$\\ell_1$ Minimization: Architecture Design and Generalization Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Boris Joukovsky, Huynh Van Luong, Nikos Deligiannis","submitted_at":"2020-03-18T17:02:10Z","abstract_excerpt":"Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. These networks have been shown to achieve faster convergence and higher accuracy than the original optimization methods. In this line of research, this paper develops a novel deep recurrent neural network (coined reweighted-RNN) by the unfolding of a reweighted $\\ell_1$-$\\ell_1$ minimization algorithm and applies it to the task of sequential signal reconstruction. To the best of our knowledge, this is the first deep un"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08334","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/2003.08334/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":"2026-07-05T00:49:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X5qDEgZb0mMtsHdsmfj9WlgUDrQsdBrYETvhoVeU7UsIb1EwMMoifcrtFSJG6iY1u6pn9Nxy0TS5WvMthG8WCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T15:28:59.660103Z"},"content_sha256":"d72c24a392f18a76aec3b107bc69e2f0840ddb47199b331ee361ab667b4c5cd3","schema_version":"1.0","event_id":"sha256:d72c24a392f18a76aec3b107bc69e2f0840ddb47199b331ee361ab667b4c5cd3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/bundle.json","state_url":"https://pith.science/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/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":"2026-08-06T15:28:59Z","links":{"resolver":"https://pith.science/pith/VDA3PNLBO6CZL23T2ZRJRF74IF","bundle":"https://pith.science/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/bundle.json","state":"https://pith.science/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VDA3PNLBO6CZL23T2ZRJRF74IF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:VDA3PNLBO6CZL23T2ZRJRF74IF","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":"09e9cb1595a6c62b56d9e2e1ab4e14fd62987a24316d0a12fcb1c07270f1c6e2","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-18T17:02:10Z","title_canon_sha256":"6c6166c35f087a5325bb87915f30ad0d3ec9483de58fb7a785cd733f4f6c1e06"},"schema_version":"1.0","source":{"id":"2003.08334","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2003.08334","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"arxiv_version","alias_value":"2003.08334v1","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.08334","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_12","alias_value":"VDA3PNLBO6CZ","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_16","alias_value":"VDA3PNLBO6CZL23T","created_at":"2026-07-05T00:49:06Z"},{"alias_kind":"pith_short_8","alias_value":"VDA3PNLB","created_at":"2026-07-05T00:49:06Z"}],"graph_snapshots":[{"event_id":"sha256:d72c24a392f18a76aec3b107bc69e2f0840ddb47199b331ee361ab667b4c5cd3","target":"graph","created_at":"2026-07-05T00:49:06Z","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/2003.08334/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. These networks have been shown to achieve faster convergence and higher accuracy than the original optimization methods. In this line of research, this paper develops a novel deep recurrent neural network (coined reweighted-RNN) by the unfolding of a reweighted $\\ell_1$-$\\ell_1$ minimization algorithm and applies it to the task of sequential signal reconstruction. To the best of our knowledge, this is the first deep un","authors_text":"Boris Joukovsky, Huynh Van Luong, Nikos Deligiannis","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-18T17:02:10Z","title":"Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted $\\ell_1$-$\\ell_1$ Minimization: Architecture Design and Generalization Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.08334","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":"sha256:38789fe09cff42d05fe106d884faf8a12dcc1402d8b4aa8735083f48379c4e53","target":"record","created_at":"2026-07-05T00:49:06Z","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":"09e9cb1595a6c62b56d9e2e1ab4e14fd62987a24316d0a12fcb1c07270f1c6e2","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-18T17:02:10Z","title_canon_sha256":"6c6166c35f087a5325bb87915f30ad0d3ec9483de58fb7a785cd733f4f6c1e06"},"schema_version":"1.0","source":{"id":"2003.08334","kind":"arxiv","version":1}},"canonical_sha256":"a8c1b7b561778595eb73d6629897fc4178dff43d97850fa8a4da1f9a77558379","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a8c1b7b561778595eb73d6629897fc4178dff43d97850fa8a4da1f9a77558379","first_computed_at":"2026-07-05T00:49:06.115403Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:49:06.115403Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ZFO9rKkb4RSB9ud+6nohDfoOM/Vb3SbvqMhBxp3eQXzlb1pGh+WkX0/eIsd51Jq91B/cegi1oZXcP6breLlxBw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:49:06.115821Z","signed_message":"canonical_sha256_bytes"},"source_id":"2003.08334","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38789fe09cff42d05fe106d884faf8a12dcc1402d8b4aa8735083f48379c4e53","sha256:d72c24a392f18a76aec3b107bc69e2f0840ddb47199b331ee361ab667b4c5cd3"],"state_sha256":"31ae15da6647ba0195e26403cd69d4ea4525456d4be5d6701ff7b1633bf53809"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6ueH3StUDU8hQ4DrbsqbGOTYV/qU+qiBWMb82kgBqAcd3NmCBhexQ4NUtlhxkh1Lk8+r1B4yuBqBaALg2NGeAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T15:28:59.663767Z","bundle_sha256":"25f3630bf0ae71a3aae86a2dff5e19d0ef3ebc1104fc1da3a7e5bb51ae8808c8"}}