{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:NK2CMZNPKWIAN4UITWONPOQRKA","short_pith_number":"pith:NK2CMZNP","canonical_record":{"source":{"id":"1910.02912","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-07T16:59:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2c3abc84111d4826607c80343ba789ab73d88f80fe54db9833a8b85c19821e6d","abstract_canon_sha256":"ff8d67da328bcb2f9a733ab802b39bb633ac9266d4cbe92dccc29025eac59992"},"schema_version":"1.0"},"canonical_sha256":"6ab42665af559006f2889d9cd7ba1150278df7a9d72d19c785ee8b6e6f146956","source":{"kind":"arxiv","id":"1910.02912","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02912","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02912v1","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02912","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"NK2CMZNPKWIA","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"NK2CMZNPKWIAN4UI","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"NK2CMZNP","created_at":"2026-07-05T00:10:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:NK2CMZNPKWIAN4UITWONPOQRKA","target":"record","payload":{"canonical_record":{"source":{"id":"1910.02912","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-07T16:59:59Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"2c3abc84111d4826607c80343ba789ab73d88f80fe54db9833a8b85c19821e6d","abstract_canon_sha256":"ff8d67da328bcb2f9a733ab802b39bb633ac9266d4cbe92dccc29025eac59992"},"schema_version":"1.0"},"canonical_sha256":"6ab42665af559006f2889d9cd7ba1150278df7a9d72d19c785ee8b6e6f146956","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:10:12.403047Z","signature_b64":"wXYHqCvpVtrb3hMc1kK4FFpiWthud4a+IOmdkyHzDTTdWNwPep0iKXS9vXA/8vj+ZYBGUE4HiFJfuLoAU2FMAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6ab42665af559006f2889d9cd7ba1150278df7a9d72d19c785ee8b6e6f146956","last_reissued_at":"2026-07-05T00:10:12.402709Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:10:12.402709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.02912","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":"2026-07-05T00:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yu8rrq27jhRcKX4a3d0UvVcGMqrpJubOjuTZopjSNC/iNJfLqWXRG/IaVtc51YLP9eGDCo/sclxmVY7064F+Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:20:26.175260Z"},"content_sha256":"6998a93b3019361cc108e911d4e59fc6ec1f2c17759dd20889dcf63b0ddd07d8","schema_version":"1.0","event_id":"sha256:6998a93b3019361cc108e911d4e59fc6ec1f2c17759dd20889dcf63b0ddd07d8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:NK2CMZNPKWIAN4UITWONPOQRKA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Increasing Expressivity of a Hyperspherical VAE","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Efstratios Gavves, Jakub M. Tomczak, Tim R. Davidson","submitted_at":"2019-10-07T16:59:59Z","abstract_excerpt":"Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally the Gaussian normal distribution has been the go-to latent parameterization, recently a variety of works have successfully proposed the use of manifold-valued latents. In one such work (Davidson et al., 2018), the authors empirically show the potential benefits of using a hyperspherical von Mises-Fisher (vMF) distribution in low dimensionality. However, due to the unique distributional form of the vMF, expressivity "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02912","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/1910.02912/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":"2026-07-05T00:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7vSuqE0kJC71muBeV4TH/zAhHdDmM+PMgj/ylDnOvn7exzhch4Az+JTsBr7hiOXz4PWLtE3lW0xxuRRF2Y7/BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T11:20:26.176176Z"},"content_sha256":"5acfb8c4f529e58f6aac44f6da1851ebeb8e17ce6f6e21354d0bd4556b430b35","schema_version":"1.0","event_id":"sha256:5acfb8c4f529e58f6aac44f6da1851ebeb8e17ce6f6e21354d0bd4556b430b35"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NK2CMZNPKWIAN4UITWONPOQRKA/bundle.json","state_url":"https://pith.science/pith/NK2CMZNPKWIAN4UITWONPOQRKA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NK2CMZNPKWIAN4UITWONPOQRKA/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":"2026-08-08T11:20:26Z","links":{"resolver":"https://pith.science/pith/NK2CMZNPKWIAN4UITWONPOQRKA","bundle":"https://pith.science/pith/NK2CMZNPKWIAN4UITWONPOQRKA/bundle.json","state":"https://pith.science/pith/NK2CMZNPKWIAN4UITWONPOQRKA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NK2CMZNPKWIAN4UITWONPOQRKA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:NK2CMZNPKWIAN4UITWONPOQRKA","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":"ff8d67da328bcb2f9a733ab802b39bb633ac9266d4cbe92dccc29025eac59992","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-07T16:59:59Z","title_canon_sha256":"2c3abc84111d4826607c80343ba789ab73d88f80fe54db9833a8b85c19821e6d"},"schema_version":"1.0","source":{"id":"1910.02912","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02912","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02912v1","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02912","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"NK2CMZNPKWIA","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"NK2CMZNPKWIAN4UI","created_at":"2026-07-05T00:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"NK2CMZNP","created_at":"2026-07-05T00:10:12Z"}],"graph_snapshots":[{"event_id":"sha256:5acfb8c4f529e58f6aac44f6da1851ebeb8e17ce6f6e21354d0bd4556b430b35","target":"graph","created_at":"2026-07-05T00:10:12Z","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/1910.02912/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning. While traditionally the Gaussian normal distribution has been the go-to latent parameterization, recently a variety of works have successfully proposed the use of manifold-valued latents. In one such work (Davidson et al., 2018), the authors empirically show the potential benefits of using a hyperspherical von Mises-Fisher (vMF) distribution in low dimensionality. However, due to the unique distributional form of the vMF, expressivity ","authors_text":"Efstratios Gavves, Jakub M. Tomczak, Tim R. Davidson","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-07T16:59:59Z","title":"Increasing Expressivity of a Hyperspherical VAE"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02912","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":"sha256:6998a93b3019361cc108e911d4e59fc6ec1f2c17759dd20889dcf63b0ddd07d8","target":"record","created_at":"2026-07-05T00:10:12Z","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":"ff8d67da328bcb2f9a733ab802b39bb633ac9266d4cbe92dccc29025eac59992","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2019-10-07T16:59:59Z","title_canon_sha256":"2c3abc84111d4826607c80343ba789ab73d88f80fe54db9833a8b85c19821e6d"},"schema_version":"1.0","source":{"id":"1910.02912","kind":"arxiv","version":1}},"canonical_sha256":"6ab42665af559006f2889d9cd7ba1150278df7a9d72d19c785ee8b6e6f146956","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6ab42665af559006f2889d9cd7ba1150278df7a9d72d19c785ee8b6e6f146956","first_computed_at":"2026-07-05T00:10:12.402709Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:10:12.402709Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wXYHqCvpVtrb3hMc1kK4FFpiWthud4a+IOmdkyHzDTTdWNwPep0iKXS9vXA/8vj+ZYBGUE4HiFJfuLoAU2FMAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:10:12.403047Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02912","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6998a93b3019361cc108e911d4e59fc6ec1f2c17759dd20889dcf63b0ddd07d8","sha256:5acfb8c4f529e58f6aac44f6da1851ebeb8e17ce6f6e21354d0bd4556b430b35"],"state_sha256":"b2cb8fa541fd333cb33843d4dc3f4018770dccb5cd218eff2cc15ed8a03eab94"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wo6qhGtxyLyYpy/7qZm+X9mopwUXpDuJbCLwgz+va5prm9fiRkd5yRV+M+LReaqc2ad+8/1KvwVsTY6OaUR0BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T11:20:26.182242Z","bundle_sha256":"32e355e14cc3f56f4fa0de3740dea51282bf93365e302d9e50a7f6f65053be57"}}