{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:ZLCDG574IJIHFJFHT44HYPHNBV","short_pith_number":"pith:ZLCDG574","canonical_record":{"source":{"id":"1707.03450","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T20:03:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e9683ed20a5e9e6fc2350a7fe26d42f920dbc65b595446809e5e0d6cbb945ac","abstract_canon_sha256":"058f7bfe0fe0bad2e4dde6deb20475d647f3ea2564d8814023e690ff6686aaab"},"schema_version":"1.0"},"canonical_sha256":"cac43377fc425072a4a79f387c3ced0d5f774537133d1f3490e702b8f930b607","source":{"kind":"arxiv","id":"1707.03450","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.03450","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"arxiv_version","alias_value":"1707.03450v1","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03450","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"pith_short_12","alias_value":"ZLCDG574IJIH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZLCDG574IJIHFJFH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZLCDG574","created_at":"2026-05-18T12:31:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:ZLCDG574IJIHFJFHT44HYPHNBV","target":"record","payload":{"canonical_record":{"source":{"id":"1707.03450","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T20:03:31Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0e9683ed20a5e9e6fc2350a7fe26d42f920dbc65b595446809e5e0d6cbb945ac","abstract_canon_sha256":"058f7bfe0fe0bad2e4dde6deb20475d647f3ea2564d8814023e690ff6686aaab"},"schema_version":"1.0"},"canonical_sha256":"cac43377fc425072a4a79f387c3ced0d5f774537133d1f3490e702b8f930b607","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:56.356146Z","signature_b64":"169X++wbvCdFKO5AoyX+a+iLxY861zPoHMLODnOOayfyhQRXalHUAjT0DSsIIE1ECQrxeDDM8Trfn2ZrPgiRAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cac43377fc425072a4a79f387c3ced0d5f774537133d1f3490e702b8f930b607","last_reissued_at":"2026-05-18T00:39:56.355499Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:56.355499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1707.03450","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":"2026-05-18T00:39:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zU3ORbTpI1C2b95m8r4nq0jMZ7TpPePVyXiaUTRXD7RsVUNSKv8o9IMu2BCQxLx3x8PORo3lS0lKJcMh44NbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T12:19:05.972111Z"},"content_sha256":"fadd082a5249d20722f2b7dcb77b164abb168984253da1cd76abf9f3ee074b3d","schema_version":"1.0","event_id":"sha256:fadd082a5249d20722f2b7dcb77b164abb168984253da1cd76abf9f3ee074b3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:ZLCDG574IJIHFJFHT44HYPHNBV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Initialising Kernel Adaptive Filters via Probabilistic Inference","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Crist\\'obal Silva, Felipe Tobar, Iv\\'an Castro","submitted_at":"2017-07-11T20:03:31Z","abstract_excerpt":"We present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights and dictionary, enforcing desired properties such as sparsity. The model can then be trained using a subset of data to initialise standard KAFs or updated sequentially each time a new observation bec"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03450","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":"2026-05-18T00:39:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"T8p79qj81REjsP4xLM5AZBLiA9cymp1Pe4YsALL/9+Krrmc0d16Sxp24IrO3VC6Ch+qnXPUtKOfe5fkUi9h7Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T12:19:05.972468Z"},"content_sha256":"7d7fd6c8225c22a3b6327908cd3bbc2aa5b059dd3109dca9181c27023623b972","schema_version":"1.0","event_id":"sha256:7d7fd6c8225c22a3b6327908cd3bbc2aa5b059dd3109dca9181c27023623b972"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZLCDG574IJIHFJFHT44HYPHNBV/bundle.json","state_url":"https://pith.science/pith/ZLCDG574IJIHFJFHT44HYPHNBV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZLCDG574IJIHFJFHT44HYPHNBV/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":"2026-06-29T12:19:05Z","links":{"resolver":"https://pith.science/pith/ZLCDG574IJIHFJFHT44HYPHNBV","bundle":"https://pith.science/pith/ZLCDG574IJIHFJFHT44HYPHNBV/bundle.json","state":"https://pith.science/pith/ZLCDG574IJIHFJFHT44HYPHNBV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZLCDG574IJIHFJFHT44HYPHNBV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:ZLCDG574IJIHFJFHT44HYPHNBV","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":"058f7bfe0fe0bad2e4dde6deb20475d647f3ea2564d8814023e690ff6686aaab","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T20:03:31Z","title_canon_sha256":"0e9683ed20a5e9e6fc2350a7fe26d42f920dbc65b595446809e5e0d6cbb945ac"},"schema_version":"1.0","source":{"id":"1707.03450","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1707.03450","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"arxiv_version","alias_value":"1707.03450v1","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.03450","created_at":"2026-05-18T00:39:56Z"},{"alias_kind":"pith_short_12","alias_value":"ZLCDG574IJIH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_16","alias_value":"ZLCDG574IJIHFJFH","created_at":"2026-05-18T12:31:59Z"},{"alias_kind":"pith_short_8","alias_value":"ZLCDG574","created_at":"2026-05-18T12:31:59Z"}],"graph_snapshots":[{"event_id":"sha256:7d7fd6c8225c22a3b6327908cd3bbc2aa5b059dd3109dca9181c27023623b972","target":"graph","created_at":"2026-05-18T00:39:56Z","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 present a probabilistic framework for both (i) determining the initial settings of kernel adaptive filters (KAFs) and (ii) constructing fully-adaptive KAFs whereby in addition to weights and dictionaries, kernel parameters are learnt sequentially. This is achieved by formulating the estimator as a probabilistic model and defining dedicated prior distributions over the kernel parameters, weights and dictionary, enforcing desired properties such as sparsity. The model can then be trained using a subset of data to initialise standard KAFs or updated sequentially each time a new observation bec","authors_text":"Crist\\'obal Silva, Felipe Tobar, Iv\\'an Castro","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T20:03:31Z","title":"Initialising Kernel Adaptive Filters via Probabilistic Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.03450","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:fadd082a5249d20722f2b7dcb77b164abb168984253da1cd76abf9f3ee074b3d","target":"record","created_at":"2026-05-18T00:39:56Z","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":"058f7bfe0fe0bad2e4dde6deb20475d647f3ea2564d8814023e690ff6686aaab","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-07-11T20:03:31Z","title_canon_sha256":"0e9683ed20a5e9e6fc2350a7fe26d42f920dbc65b595446809e5e0d6cbb945ac"},"schema_version":"1.0","source":{"id":"1707.03450","kind":"arxiv","version":1}},"canonical_sha256":"cac43377fc425072a4a79f387c3ced0d5f774537133d1f3490e702b8f930b607","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"cac43377fc425072a4a79f387c3ced0d5f774537133d1f3490e702b8f930b607","first_computed_at":"2026-05-18T00:39:56.355499Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:39:56.355499Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"169X++wbvCdFKO5AoyX+a+iLxY861zPoHMLODnOOayfyhQRXalHUAjT0DSsIIE1ECQrxeDDM8Trfn2ZrPgiRAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:39:56.356146Z","signed_message":"canonical_sha256_bytes"},"source_id":"1707.03450","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fadd082a5249d20722f2b7dcb77b164abb168984253da1cd76abf9f3ee074b3d","sha256:7d7fd6c8225c22a3b6327908cd3bbc2aa5b059dd3109dca9181c27023623b972"],"state_sha256":"a08b6e3f6af68deac0a70a91a999e2f66bb2da04a215455f3a183b6a0505ccde"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XYa8ZxjSHa+PuZkc7NlqiL763jzkvluHiFOfWv9ZPEiUalowMlZ24a+S06dZfcZUpQu8s3IHjHv8dosBYmYpDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T12:19:05.974293Z","bundle_sha256":"3a1d8e3b401853d26ee6bb96a45fc9b8fedfcd08cd7eee1c90a08177222ec62f"}}