{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:U2KKH7LSD4VLXMR3QT4RN7IICK","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":"b942f44654a306ae87a5d2433a183de5fbcfba1fd19763d039a6e4853ca4b441","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-17T07:01:41Z","title_canon_sha256":"5641e24d5304f527b51df61cbd1fb1f7ef05e9faac8cbe4a2b0d10b7cd4095c0"},"schema_version":"1.0","source":{"id":"2404.11117","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2404.11117","created_at":"2026-07-05T08:09:04Z"},{"alias_kind":"arxiv_version","alias_value":"2404.11117v1","created_at":"2026-07-05T08:09:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.11117","created_at":"2026-07-05T08:09:04Z"},{"alias_kind":"pith_short_12","alias_value":"U2KKH7LSD4VL","created_at":"2026-07-05T08:09:04Z"},{"alias_kind":"pith_short_16","alias_value":"U2KKH7LSD4VLXMR3","created_at":"2026-07-05T08:09:04Z"},{"alias_kind":"pith_short_8","alias_value":"U2KKH7LS","created_at":"2026-07-05T08:09:04Z"}],"graph_snapshots":[{"event_id":"sha256:1b5541365b407fee15678a7e640783f42d3b734fe931cecb4172a87e64b09e58","target":"graph","created_at":"2026-07-05T08:09:04Z","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/2404.11117/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Forecasting tasks using large datasets gathering thousands of heterogeneous time series is a crucial statistical problem in numerous sectors. The main challenge is to model a rich variety of time series, leverage any available external signals and provide sharp predictions with statistical guarantees. In this work, we propose a new forecasting model that combines discrete state space hidden Markov models with recent neural network architectures and training procedures inspired by vector quantized variational autoencoders. We introduce a variational discrete posterior distribution of the latent","authors_text":"Etienne David (IP Paris, ISTeC-SAMOVAR), Jean Bellot, SU), Sylvain Le Corff (LPSM (UMR\\_8001)","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-17T07:01:41Z","title":"Variational quantization for state space models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.11117","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:d4586d3336a965dc96c161db4a11533074fabc6814668d9d9fb8723ed909e3d1","target":"record","created_at":"2026-07-05T08:09:04Z","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":"b942f44654a306ae87a5d2433a183de5fbcfba1fd19763d039a6e4853ca4b441","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-04-17T07:01:41Z","title_canon_sha256":"5641e24d5304f527b51df61cbd1fb1f7ef05e9faac8cbe4a2b0d10b7cd4095c0"},"schema_version":"1.0","source":{"id":"2404.11117","kind":"arxiv","version":1}},"canonical_sha256":"a694a3fd721f2abbb23b84f916fd08128dcca798a58df59e5a71d54dfcc8f36d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a694a3fd721f2abbb23b84f916fd08128dcca798a58df59e5a71d54dfcc8f36d","first_computed_at":"2026-07-05T08:09:04.312255Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:09:04.312255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AAzSS4HCGNzrpWnV92VEOhk25PVH+nPGhgm6t1gYXu8gh8/zc7H716TJYgwoldMsRvrOr3ac7LchrMcxGNu1DA==","signature_status":"signed_v1","signed_at":"2026-07-05T08:09:04.312723Z","signed_message":"canonical_sha256_bytes"},"source_id":"2404.11117","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4586d3336a965dc96c161db4a11533074fabc6814668d9d9fb8723ed909e3d1","sha256:1b5541365b407fee15678a7e640783f42d3b734fe931cecb4172a87e64b09e58"],"state_sha256":"681833e2be79fb96583329a189f01286fd2a09c89eb80fd7136f7cf41f1a68bb"}