{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KO3BAUACI4DY7QX4TFZWHBTDWX","short_pith_number":"pith:KO3BAUAC","schema_version":"1.0","canonical_sha256":"53b610500247078fc2fc9973638663b5c5962f980ca92a23120736a419d0ba09","source":{"kind":"arxiv","id":"1702.05441","version":1},"attestation_state":"computed","paper":{"title":"Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Angelo Cangelosi, Junpei Zhong, Tetsuya Ogata","submitted_at":"2017-02-07T09:31:41Z","abstract_excerpt":"The abstraction tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain. Recently, inspired from Multiple Time-scale Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU), called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been propo"},"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":"1702.05441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-02-07T09:31:41Z","cross_cats_sorted":[],"title_canon_sha256":"dee5f6d9f559bcaf4b887a55de07fae4a5ab1b40eb2d8ff6a143753a48b61450","abstract_canon_sha256":"3aed28981a962e86338a003f9caaa91f080ea5d62ad192dfff34cf2034e6738a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:31.723693Z","signature_b64":"LwaASi6Awsv3G5fgvye1p5nllAVggvOZkMPBsDN7twPrukbXVkR9meZ7APGDc/wAz96tjcjbp23qD6XwlwTADA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"53b610500247078fc2fc9973638663b5c5962f980ca92a23120736a419d0ba09","last_reissued_at":"2026-05-18T00:50:31.722978Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:31.722978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Toward Abstraction from Multi-modal Data: Empirical Studies on Multiple Time-scale Recurrent Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.NE","authors_text":"Angelo Cangelosi, Junpei Zhong, Tetsuya Ogata","submitted_at":"2017-02-07T09:31:41Z","abstract_excerpt":"The abstraction tasks are challenging for multi- modal sequences as they require a deeper semantic understanding and a novel text generation for the data. Although the recurrent neural networks (RNN) can be used to model the context of the time-sequences, in most cases the long-term dependencies of multi-modal data make the back-propagation through time training of RNN tend to vanish in the time domain. Recently, inspired from Multiple Time-scale Recurrent Neural Network (MTRNN), an extension of Gated Recurrent Unit (GRU), called Multiple Time-scale Gated Recurrent Unit (MTGRU), has been propo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.05441","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":""},"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":"1702.05441","created_at":"2026-05-18T00:50:31.723080+00:00"},{"alias_kind":"arxiv_version","alias_value":"1702.05441v1","created_at":"2026-05-18T00:50:31.723080+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.05441","created_at":"2026-05-18T00:50:31.723080+00:00"},{"alias_kind":"pith_short_12","alias_value":"KO3BAUACI4DY","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_16","alias_value":"KO3BAUACI4DY7QX4","created_at":"2026-05-18T12:31:24.725408+00:00"},{"alias_kind":"pith_short_8","alias_value":"KO3BAUAC","created_at":"2026-05-18T12:31:24.725408+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/KO3BAUACI4DY7QX4TFZWHBTDWX","json":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX.json","graph_json":"https://pith.science/api/pith-number/KO3BAUACI4DY7QX4TFZWHBTDWX/graph.json","events_json":"https://pith.science/api/pith-number/KO3BAUACI4DY7QX4TFZWHBTDWX/events.json","paper":"https://pith.science/paper/KO3BAUAC"},"agent_actions":{"view_html":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX","download_json":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX.json","view_paper":"https://pith.science/paper/KO3BAUAC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1702.05441&json=true","fetch_graph":"https://pith.science/api/pith-number/KO3BAUACI4DY7QX4TFZWHBTDWX/graph.json","fetch_events":"https://pith.science/api/pith-number/KO3BAUACI4DY7QX4TFZWHBTDWX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX/action/storage_attestation","attest_author":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX/action/author_attestation","sign_citation":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX/action/citation_signature","submit_replication":"https://pith.science/pith/KO3BAUACI4DY7QX4TFZWHBTDWX/action/replication_record"}},"created_at":"2026-05-18T00:50:31.723080+00:00","updated_at":"2026-05-18T00:50:31.723080+00:00"}