{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:DYLWVOOHOHA37KP7ECIP2R3GM5","short_pith_number":"pith:DYLWVOOH","schema_version":"1.0","canonical_sha256":"1e176ab9c771c1bfa9ff2090fd47666766c4d1fc664719de3de5757760f12fe7","source":{"kind":"arxiv","id":"2106.11197","version":1},"attestation_state":"computed","paper":{"title":"Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Binzong Geng, Fajie Yuan, Min Yang, Ruifeng Xu, Shupeng Wang, Xiang Ao","submitted_at":"2021-06-21T15:34:13Z","abstract_excerpt":"Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with un"},"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":"2106.11197","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2021-06-21T15:34:13Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"629f75df217702247cc530774d9ff8107095cc77660871d0acc198197be59315","abstract_canon_sha256":"e0f2f9de734c8cebaba8ef6e3c9908a5f2c3f12faefca7216ceb3f54b8a92391"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:50:57.716095Z","signature_b64":"EyY1afYoNfMMCxbVhcL8ZvqNiVhbaOiLxEWidagbX6h9/xi912u17W5twsR78d90nW2t5LXP4V8fmTYeTVC5Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e176ab9c771c1bfa9ff2090fd47666766c4d1fc664719de3de5757760f12fe7","last_reissued_at":"2026-07-05T02:50:57.715644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:50:57.715644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Binzong Geng, Fajie Yuan, Min Yang, Ruifeng Xu, Shupeng Wang, Xiang Ao","submitted_at":"2021-06-21T15:34:13Z","abstract_excerpt":"Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non-trivial for deep neural networks as continually training of incrementally available information inevitably results in catastrophic forgetting or interference. In this paper, we propose a novel iterative network pruning with uncertainty regularization method for lifelong sentiment classification (IPRLS), which leverages the principles of network pruning and weight regularization. By performing network pruning with un"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2106.11197","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/2106.11197/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":"2106.11197","created_at":"2026-07-05T02:50:57.715720+00:00"},{"alias_kind":"arxiv_version","alias_value":"2106.11197v1","created_at":"2026-07-05T02:50:57.715720+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2106.11197","created_at":"2026-07-05T02:50:57.715720+00:00"},{"alias_kind":"pith_short_12","alias_value":"DYLWVOOHOHA3","created_at":"2026-07-05T02:50:57.715720+00:00"},{"alias_kind":"pith_short_16","alias_value":"DYLWVOOHOHA37KP7","created_at":"2026-07-05T02:50:57.715720+00:00"},{"alias_kind":"pith_short_8","alias_value":"DYLWVOOH","created_at":"2026-07-05T02:50:57.715720+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5","json":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5.json","graph_json":"https://pith.science/api/pith-number/DYLWVOOHOHA37KP7ECIP2R3GM5/graph.json","events_json":"https://pith.science/api/pith-number/DYLWVOOHOHA37KP7ECIP2R3GM5/events.json","paper":"https://pith.science/paper/DYLWVOOH"},"agent_actions":{"view_html":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5","download_json":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5.json","view_paper":"https://pith.science/paper/DYLWVOOH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2106.11197&json=true","fetch_graph":"https://pith.science/api/pith-number/DYLWVOOHOHA37KP7ECIP2R3GM5/graph.json","fetch_events":"https://pith.science/api/pith-number/DYLWVOOHOHA37KP7ECIP2R3GM5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5/action/storage_attestation","attest_author":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5/action/author_attestation","sign_citation":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5/action/citation_signature","submit_replication":"https://pith.science/pith/DYLWVOOHOHA37KP7ECIP2R3GM5/action/replication_record"}},"created_at":"2026-07-05T02:50:57.715720+00:00","updated_at":"2026-07-05T02:50:57.715720+00:00"}