{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:PE5MGRI3P6Y3EAQESOSHIK7EY5","short_pith_number":"pith:PE5MGRI3","canonical_record":{"source":{"id":"1912.02644","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T15:27:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2b1319b42617bc08236c69d2b83c223fc9fde9c0c92c511503691acbfa1e8361","abstract_canon_sha256":"c95f6cc29a41d135ce3e18e0c2e1bb7341ca6828d74f66ac4dcd76bc1bd77291"},"schema_version":"1.0"},"canonical_sha256":"793ac3451b7fb1b2020493a4742be4c764ce652180a6149067e8f248cf5580cb","source":{"kind":"arxiv","id":"1912.02644","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.02644","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"arxiv_version","alias_value":"1912.02644v1","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.02644","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_12","alias_value":"PE5MGRI3P6Y3","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_16","alias_value":"PE5MGRI3P6Y3EAQE","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_8","alias_value":"PE5MGRI3","created_at":"2026-07-05T00:24:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:PE5MGRI3P6Y3EAQESOSHIK7EY5","target":"record","payload":{"canonical_record":{"source":{"id":"1912.02644","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T15:27:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2b1319b42617bc08236c69d2b83c223fc9fde9c0c92c511503691acbfa1e8361","abstract_canon_sha256":"c95f6cc29a41d135ce3e18e0c2e1bb7341ca6828d74f66ac4dcd76bc1bd77291"},"schema_version":"1.0"},"canonical_sha256":"793ac3451b7fb1b2020493a4742be4c764ce652180a6149067e8f248cf5580cb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:24:15.591938Z","signature_b64":"3JcRsRuHp0u2NB7bmSggvLi/UAZbHmamTDwlG7LMl/2g5SQ5KqiSTC+chHVi89VJ3IFQ7Wf0lD8BuMAjm8psAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"793ac3451b7fb1b2020493a4742be4c764ce652180a6149067e8f248cf5580cb","last_reissued_at":"2026-07-05T00:24:15.591520Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:24:15.591520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1912.02644","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":"2026-07-05T00:24:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8ReeXuIkHPY4DXqNG8f124vri3Yr7xnQS16NybvKJG5Cw+Lo0DcVJ2qyvvPjqBt2dCSKqwA4BgVlX5BW9pp1Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:53:17.116323Z"},"content_sha256":"435cf42e6e741edc76a7fa6381b2d224c5b672202d77014aabf3b25cc5d1c52b","schema_version":"1.0","event_id":"sha256:435cf42e6e741edc76a7fa6381b2d224c5b672202d77014aabf3b25cc5d1c52b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:PE5MGRI3P6Y3EAQESOSHIK7EY5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Representing Closed Transformation Paths in Encoded Network Latent Space","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Christopher Rozell, Marissa Connor","submitted_at":"2019-12-05T15:27:26Z","abstract_excerpt":"Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space. In many cases, data transformations are defined by linear paths in this latent space. However, the Euclidean structure of the latent space may be a poor match for the underlying latent structure in the data. In this work, we incorporate a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latent space to accommodate this structure. In particular, we focus on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.02644","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/1912.02644/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":"2026-07-05T00:24:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NCuX3v/n1CjO7HUDNLH+mhyDkj0dsNwREm3XSqb/U2SEfViBoIrRL6onQrbcHo7vvirUAqf0b0XFi1FG/PzYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T16:53:17.116832Z"},"content_sha256":"0233703389d5eaad9348710f0359e2891a05d95c72c0caeafab674421f23f3fc","schema_version":"1.0","event_id":"sha256:0233703389d5eaad9348710f0359e2891a05d95c72c0caeafab674421f23f3fc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/bundle.json","state_url":"https://pith.science/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/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":"2026-08-04T16:53:17Z","links":{"resolver":"https://pith.science/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5","bundle":"https://pith.science/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/bundle.json","state":"https://pith.science/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/PE5MGRI3P6Y3EAQESOSHIK7EY5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:PE5MGRI3P6Y3EAQESOSHIK7EY5","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":"c95f6cc29a41d135ce3e18e0c2e1bb7341ca6828d74f66ac4dcd76bc1bd77291","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T15:27:26Z","title_canon_sha256":"2b1319b42617bc08236c69d2b83c223fc9fde9c0c92c511503691acbfa1e8361"},"schema_version":"1.0","source":{"id":"1912.02644","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1912.02644","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"arxiv_version","alias_value":"1912.02644v1","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1912.02644","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_12","alias_value":"PE5MGRI3P6Y3","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_16","alias_value":"PE5MGRI3P6Y3EAQE","created_at":"2026-07-05T00:24:15Z"},{"alias_kind":"pith_short_8","alias_value":"PE5MGRI3","created_at":"2026-07-05T00:24:15Z"}],"graph_snapshots":[{"event_id":"sha256:0233703389d5eaad9348710f0359e2891a05d95c72c0caeafab674421f23f3fc","target":"graph","created_at":"2026-07-05T00:24:15Z","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/1912.02644/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep generative networks have been widely used for learning mappings from a low-dimensional latent space to a high-dimensional data space. In many cases, data transformations are defined by linear paths in this latent space. However, the Euclidean structure of the latent space may be a poor match for the underlying latent structure in the data. In this work, we incorporate a generative manifold model into the latent space of an autoencoder in order to learn the low-dimensional manifold structure from the data and adapt the latent space to accommodate this structure. In particular, we focus on ","authors_text":"Christopher Rozell, Marissa Connor","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T15:27:26Z","title":"Representing Closed Transformation Paths in Encoded Network Latent Space"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1912.02644","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":"sha256:435cf42e6e741edc76a7fa6381b2d224c5b672202d77014aabf3b25cc5d1c52b","target":"record","created_at":"2026-07-05T00:24:15Z","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":"c95f6cc29a41d135ce3e18e0c2e1bb7341ca6828d74f66ac4dcd76bc1bd77291","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-12-05T15:27:26Z","title_canon_sha256":"2b1319b42617bc08236c69d2b83c223fc9fde9c0c92c511503691acbfa1e8361"},"schema_version":"1.0","source":{"id":"1912.02644","kind":"arxiv","version":1}},"canonical_sha256":"793ac3451b7fb1b2020493a4742be4c764ce652180a6149067e8f248cf5580cb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"793ac3451b7fb1b2020493a4742be4c764ce652180a6149067e8f248cf5580cb","first_computed_at":"2026-07-05T00:24:15.591520Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:24:15.591520Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3JcRsRuHp0u2NB7bmSggvLi/UAZbHmamTDwlG7LMl/2g5SQ5KqiSTC+chHVi89VJ3IFQ7Wf0lD8BuMAjm8psAw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:24:15.591938Z","signed_message":"canonical_sha256_bytes"},"source_id":"1912.02644","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:435cf42e6e741edc76a7fa6381b2d224c5b672202d77014aabf3b25cc5d1c52b","sha256:0233703389d5eaad9348710f0359e2891a05d95c72c0caeafab674421f23f3fc"],"state_sha256":"88734166ff7b1ce80a3533ffe8efcae8b30068d7f65c08129b91c785882f2e77"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BYMP362T0WS7gLBB2AaGmIc8cobdsYg6Fxi0vtvVfB0cYx08MEvETfgfMg2hxzAIxN6BTxUU1+V0C6wyMAW0AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T16:53:17.120781Z","bundle_sha256":"b390b65b6be57f8adde08fd2d696db4063f8d16dfb695714e10f7261aceca2d4"}}