{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:TIVV43KZZK557ZTZMIMVORL5QZ","short_pith_number":"pith:TIVV43KZ","canonical_record":{"source":{"id":"1804.10839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-28T18:38:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7d0872aecf00737a68cbb99be882e09c97f52d31115457c438a8ff8bfb5824d8","abstract_canon_sha256":"87db5f3f69f94fbda595f097e26e4b1a71aed4fdc514ce07186018e6af069fdf"},"schema_version":"1.0"},"canonical_sha256":"9a2b5e6d59cabbdfe679621957457d867801589493a3cef4b715a589787a6d92","source":{"kind":"arxiv","id":"1804.10839","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.10839","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"arxiv_version","alias_value":"1804.10839v1","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10839","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"pith_short_12","alias_value":"TIVV43KZZK55","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TIVV43KZZK557ZTZ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TIVV43KZ","created_at":"2026-05-18T12:32:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:TIVV43KZZK557ZTZMIMVORL5QZ","target":"record","payload":{"canonical_record":{"source":{"id":"1804.10839","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-28T18:38:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7d0872aecf00737a68cbb99be882e09c97f52d31115457c438a8ff8bfb5824d8","abstract_canon_sha256":"87db5f3f69f94fbda595f097e26e4b1a71aed4fdc514ce07186018e6af069fdf"},"schema_version":"1.0"},"canonical_sha256":"9a2b5e6d59cabbdfe679621957457d867801589493a3cef4b715a589787a6d92","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:15.385543Z","signature_b64":"uNxbA5/ehWDc95yi2IrnEFUWkc1hI9cgHQlhCZ51ahnneiRCyWrsoCw7TiNG9IwVlkSEoPbHLphu4nYI/LhCAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9a2b5e6d59cabbdfe679621957457d867801589493a3cef4b715a589787a6d92","last_reissued_at":"2026-05-18T00:17:15.385029Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:15.385029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1804.10839","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":"2026-05-18T00:17:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FcoXIyH966c/q2i/R73VtKuzrFKZJa2vuoFnZgmToBxNrB93JlJI1hwy9at5XWRhTk31MkCMacQZirQFFk0ECA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:08:24.044172Z"},"content_sha256":"53472f0cbeebd5f79a71a44519bea6cbc3055a9d47da561ad03aac63ec1bb9eb","schema_version":"1.0","event_id":"sha256:53472f0cbeebd5f79a71a44519bea6cbc3055a9d47da561ad03aac63ec1bb9eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:TIVV43KZZK557ZTZMIMVORL5QZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning from multivariate discrete sequential data using a restricted Boltzmann machine model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Andres G. Abad, Jefferson Hernandez","submitted_at":"2018-04-28T18:38:27Z","abstract_excerpt":"A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by proposing the p-RBM model, a generalization of the regular RBM model, capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of predicting the stock market direction considering 100 "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10839","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":"2026-05-18T00:17:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cul4ZJUx3LK+Z/QYcQNLb6r8eAgy5ZTvwHwVXoT/P/yuf/M9FYpJrFXqBEyoVoXy5SYAucXjiTECs7ixW5iMBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T22:08:24.044923Z"},"content_sha256":"614256840713a4c90ec5d571c26a0a9015e61a383d3f1c5bdcf31953ac5975d1","schema_version":"1.0","event_id":"sha256:614256840713a4c90ec5d571c26a0a9015e61a383d3f1c5bdcf31953ac5975d1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/TIVV43KZZK557ZTZMIMVORL5QZ/bundle.json","state_url":"https://pith.science/pith/TIVV43KZZK557ZTZMIMVORL5QZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/TIVV43KZZK557ZTZMIMVORL5QZ/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":"2026-06-07T22:08:24Z","links":{"resolver":"https://pith.science/pith/TIVV43KZZK557ZTZMIMVORL5QZ","bundle":"https://pith.science/pith/TIVV43KZZK557ZTZMIMVORL5QZ/bundle.json","state":"https://pith.science/pith/TIVV43KZZK557ZTZMIMVORL5QZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/TIVV43KZZK557ZTZMIMVORL5QZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:TIVV43KZZK557ZTZMIMVORL5QZ","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":"87db5f3f69f94fbda595f097e26e4b1a71aed4fdc514ce07186018e6af069fdf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-28T18:38:27Z","title_canon_sha256":"7d0872aecf00737a68cbb99be882e09c97f52d31115457c438a8ff8bfb5824d8"},"schema_version":"1.0","source":{"id":"1804.10839","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1804.10839","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"arxiv_version","alias_value":"1804.10839v1","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10839","created_at":"2026-05-18T00:17:15Z"},{"alias_kind":"pith_short_12","alias_value":"TIVV43KZZK55","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_16","alias_value":"TIVV43KZZK557ZTZ","created_at":"2026-05-18T12:32:53Z"},{"alias_kind":"pith_short_8","alias_value":"TIVV43KZ","created_at":"2026-05-18T12:32:53Z"}],"graph_snapshots":[{"event_id":"sha256:614256840713a4c90ec5d571c26a0a9015e61a383d3f1c5bdcf31953ac5975d1","target":"graph","created_at":"2026-05-18T00:17:15Z","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"},"paper":{"abstract_excerpt":"A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by proposing the p-RBM model, a generalization of the regular RBM model, capable of retaining memory of p past states. We further show how to train the p-RBM model using contrastive divergence and test our model on the problem of predicting the stock market direction considering 100 ","authors_text":"Andres G. Abad, Jefferson Hernandez","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-28T18:38:27Z","title":"Learning from multivariate discrete sequential data using a restricted Boltzmann machine model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10839","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":"sha256:53472f0cbeebd5f79a71a44519bea6cbc3055a9d47da561ad03aac63ec1bb9eb","target":"record","created_at":"2026-05-18T00:17:15Z","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":"87db5f3f69f94fbda595f097e26e4b1a71aed4fdc514ce07186018e6af069fdf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-04-28T18:38:27Z","title_canon_sha256":"7d0872aecf00737a68cbb99be882e09c97f52d31115457c438a8ff8bfb5824d8"},"schema_version":"1.0","source":{"id":"1804.10839","kind":"arxiv","version":1}},"canonical_sha256":"9a2b5e6d59cabbdfe679621957457d867801589493a3cef4b715a589787a6d92","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9a2b5e6d59cabbdfe679621957457d867801589493a3cef4b715a589787a6d92","first_computed_at":"2026-05-18T00:17:15.385029Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:17:15.385029Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uNxbA5/ehWDc95yi2IrnEFUWkc1hI9cgHQlhCZ51ahnneiRCyWrsoCw7TiNG9IwVlkSEoPbHLphu4nYI/LhCAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:17:15.385543Z","signed_message":"canonical_sha256_bytes"},"source_id":"1804.10839","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53472f0cbeebd5f79a71a44519bea6cbc3055a9d47da561ad03aac63ec1bb9eb","sha256:614256840713a4c90ec5d571c26a0a9015e61a383d3f1c5bdcf31953ac5975d1"],"state_sha256":"0b965c9740e449bc514665f508e290b6d039d800435dbfda14031e1a6b52815b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HXr5IgpuL0t4QsLmllOPEYJTEFgMZPPG4fCzCNcx5Zfamxeq11cRmRUf4uy/sbth7SYFXRGkpjzvvvYpRDumCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T22:08:24.048447Z","bundle_sha256":"4a7008a90745cf8e6e98c90db87e30d1e6d73d914c172f82f8499694f07ae1e0"}}