{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TKMOIX3PGYYVGHBKJ3DPNWCFXK","short_pith_number":"pith:TKMOIX3P","canonical_record":{"source":{"id":"1801.05407","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-16T18:44:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29c650ab8b28630b5e06fc0c1f3796e236316115961593b64761465e222e10a9","abstract_canon_sha256":"5b5c7890f3b9fb015c7a1459a4f6498e3082123aa5e55d5303f92a7bc0981f22"},"schema_version":"1.0"},"canonical_sha256":"9a98e45f6f3631531c2a4ec6f6d845ba8f5816355ae4def1d31d4fa6bfc21fd6","source":{"kind":"arxiv","id":"1801.05407","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.05407","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"arxiv_version","alias_value":"1801.05407v1","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05407","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"pith_short_12","alias_value":"TKMOIX3PGYYV","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TKMOIX3PGYYVGHBK","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TKMOIX3P","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TKMOIX3PGYYVGHBKJ3DPNWCFXK","target":"record","payload":{"canonical_record":{"source":{"id":"1801.05407","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-16T18:44:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"29c650ab8b28630b5e06fc0c1f3796e236316115961593b64761465e222e10a9","abstract_canon_sha256":"5b5c7890f3b9fb015c7a1459a4f6498e3082123aa5e55d5303f92a7bc0981f22"},"schema_version":"1.0"},"canonical_sha256":"9a98e45f6f3631531c2a4ec6f6d845ba8f5816355ae4def1d31d4fa6bfc21fd6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:46.855057Z","signature_b64":"SDmeMRMw+edzcd9ZbITSO2FK/NclyqofcLgyZlb5VBN7t6QMo/bRlC8bKSiJq2ZrjQAAZTJDdyytGuJVhhhlAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a98e45f6f3631531c2a4ec6f6d845ba8f5816355ae4def1d31d4fa6bfc21fd6","last_reissued_at":"2026-05-18T00:25:46.854324Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:46.854324Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.05407","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":"2026-05-18T00:25:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dARl0w/QpIY4a2F9Cnv1644jYWUWEoyqGsqGIfLpDWAcMKaIYYHo+cUpf3K7BVehisuyLLsGtd4DvtepTR+aBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T14:47:32.135479Z"},"content_sha256":"182467ee59beecffab83fb09831e1d9ce2e854d3094d2b66f55e8d4cba149426","schema_version":"1.0","event_id":"sha256:182467ee59beecffab83fb09831e1d9ce2e854d3094d2b66f55e8d4cba149426"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TKMOIX3PGYYVGHBKJ3DPNWCFXK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Canonically Correlated LSTMs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Corbin Rosset, Neil Mallinar","submitted_at":"2018-01-16T18:44:31Z","abstract_excerpt":"We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated subspace through which we get our final representation via projection. This work follows from previous work done on Deep Canonical Correlation (DCCA), in which deep feed-forward neural networks were used"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05407","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:25:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"02d6qJWbZafXzxAde2lwOn3mV29B019+Aywqq5HAk0r+2xPheAlLP9KJSiyx8saOaKppTQc+vqu0R4HT5zSNCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T14:47:32.136007Z"},"content_sha256":"9259b9dabe46fe4c8ba1ae24ab8d64521773fa5e014d49bfa4a525b70af2af0d","schema_version":"1.0","event_id":"sha256:9259b9dabe46fe4c8ba1ae24ab8d64521773fa5e014d49bfa4a525b70af2af0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/bundle.json","state_url":"https://pith.science/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/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":"2026-06-03T14:47:32Z","links":{"resolver":"https://pith.science/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK","bundle":"https://pith.science/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/bundle.json","state":"https://pith.science/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TKMOIX3PGYYVGHBKJ3DPNWCFXK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TKMOIX3PGYYVGHBKJ3DPNWCFXK","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":"5b5c7890f3b9fb015c7a1459a4f6498e3082123aa5e55d5303f92a7bc0981f22","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-16T18:44:31Z","title_canon_sha256":"29c650ab8b28630b5e06fc0c1f3796e236316115961593b64761465e222e10a9"},"schema_version":"1.0","source":{"id":"1801.05407","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.05407","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"arxiv_version","alias_value":"1801.05407v1","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05407","created_at":"2026-05-18T00:25:46Z"},{"alias_kind":"pith_short_12","alias_value":"TKMOIX3PGYYV","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TKMOIX3PGYYVGHBK","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TKMOIX3P","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:9259b9dabe46fe4c8ba1ae24ab8d64521773fa5e014d49bfa4a525b70af2af0d","target":"graph","created_at":"2026-05-18T00:25:46Z","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"},"paper":{"abstract_excerpt":"We examine Deep Canonically Correlated LSTMs as a way to learn nonlinear transformations of variable length sequences and embed them into a correlated, fixed dimensional space. We use LSTMs to transform multi-view time-series data non-linearly while learning temporal relationships within the data. We then perform correlation analysis on the outputs of these neural networks to find a correlated subspace through which we get our final representation via projection. This work follows from previous work done on Deep Canonical Correlation (DCCA), in which deep feed-forward neural networks were used","authors_text":"Corbin Rosset, Neil Mallinar","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-16T18:44:31Z","title":"Deep Canonically Correlated LSTMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05407","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":"sha256:182467ee59beecffab83fb09831e1d9ce2e854d3094d2b66f55e8d4cba149426","target":"record","created_at":"2026-05-18T00:25:46Z","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":"5b5c7890f3b9fb015c7a1459a4f6498e3082123aa5e55d5303f92a7bc0981f22","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-01-16T18:44:31Z","title_canon_sha256":"29c650ab8b28630b5e06fc0c1f3796e236316115961593b64761465e222e10a9"},"schema_version":"1.0","source":{"id":"1801.05407","kind":"arxiv","version":1}},"canonical_sha256":"9a98e45f6f3631531c2a4ec6f6d845ba8f5816355ae4def1d31d4fa6bfc21fd6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a98e45f6f3631531c2a4ec6f6d845ba8f5816355ae4def1d31d4fa6bfc21fd6","first_computed_at":"2026-05-18T00:25:46.854324Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:46.854324Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SDmeMRMw+edzcd9ZbITSO2FK/NclyqofcLgyZlb5VBN7t6QMo/bRlC8bKSiJq2ZrjQAAZTJDdyytGuJVhhhlAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:46.855057Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.05407","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:182467ee59beecffab83fb09831e1d9ce2e854d3094d2b66f55e8d4cba149426","sha256:9259b9dabe46fe4c8ba1ae24ab8d64521773fa5e014d49bfa4a525b70af2af0d"],"state_sha256":"30dee81b5a5991f57d237a9ac2b4b7a6a04e3510ae795d0dcaf08f963663cc2e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YAKiu19VlKO4zlPxnpTs1bD6tL5efFtsW1ZB3qNwvk9VPTEDX4DP8HtLJkkYTsQFdOqacyC3UFikX3CvJUf+DQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T14:47:32.138587Z","bundle_sha256":"ddfcc26bca4bf857be87fb9e0bf28499a9fbbbf7c93ac2b8e76eddf196552015"}}