{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:DSHOOKFAY3I53KZSFJU6LW3IXN","short_pith_number":"pith:DSHOOKFA","canonical_record":{"source":{"id":"1710.07324","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-19T19:13:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"25df62628db38c70b3444af500d6ec0140bea3558d26f97ad48947b0de56bc25","abstract_canon_sha256":"9feef4b0cc38c39439d122cdf5c399673916185a149673d9e175091ffa2290b0"},"schema_version":"1.0"},"canonical_sha256":"1c8ee728a0c6d1ddab322a69e5db68bb6aeb8d6738e49bc384f1e9eeb8f66cfb","source":{"kind":"arxiv","id":"1710.07324","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07324","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07324v2","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07324","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"DSHOOKFAY3I5","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DSHOOKFAY3I53KZS","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DSHOOKFA","created_at":"2026-05-18T12:31:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:DSHOOKFAY3I53KZSFJU6LW3IXN","target":"record","payload":{"canonical_record":{"source":{"id":"1710.07324","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-19T19:13:26Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"25df62628db38c70b3444af500d6ec0140bea3558d26f97ad48947b0de56bc25","abstract_canon_sha256":"9feef4b0cc38c39439d122cdf5c399673916185a149673d9e175091ffa2290b0"},"schema_version":"1.0"},"canonical_sha256":"1c8ee728a0c6d1ddab322a69e5db68bb6aeb8d6738e49bc384f1e9eeb8f66cfb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:41.652577Z","signature_b64":"kilCe5MgKBnncGRRn5rOAsK/wL2A+U2ptEs1Hj7TKRLeMhBw5eiJunzav87NK3BPOnOMkL+5T4p2N2NfTwgVCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c8ee728a0c6d1ddab322a69e5db68bb6aeb8d6738e49bc384f1e9eeb8f66cfb","last_reissued_at":"2026-05-18T00:25:41.651992Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:41.651992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.07324","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5mKwFZq3mzhns2BGg588C84G/qJ2AOVXTLQxkEOJbbeu/KfTIo2WHr5A6DxgO9itI/tZtyUKiLFTbXr2er80BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:04:50.515206Z"},"content_sha256":"2a727fb3a9e416c9c875c477132e7d177f535152d75a24605604efd8e14e852d","schema_version":"1.0","event_id":"sha256:2a727fb3a9e416c9c875c477132e7d177f535152d75a24605604efd8e14e852d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:DSHOOKFAY3I53KZSFJU6LW3IXN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Alexander Novikov, Dmitry Kropotov, Pavel Izmailov","submitted_at":"2017-10-19T19:13:26Z","abstract_excerpt":"We propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs with billions of inducing inputs and achieve state-of-the-art results on several benchmarks. Further, our approach allows for training kernels based on deep neural networks without any modifications to the underlying GP model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07324","kind":"arxiv","version":2},"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":"2026-05-18T00:25:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ViYDbof/ilT7TRx7Uj1/7TYhF8ddAIp7jUGIbj4OKnnIScE/dPoXdMt4iBoDGY+LCjRp7NOPBeJBpXeWDy9WDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T16:04:50.515908Z"},"content_sha256":"0ff35bb0b0e8bc0177ddcfd5070bb465b06a651226b7361848dc34fd8ec124c5","schema_version":"1.0","event_id":"sha256:0ff35bb0b0e8bc0177ddcfd5070bb465b06a651226b7361848dc34fd8ec124c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/bundle.json","state_url":"https://pith.science/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/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":"2026-05-25T16:04:50Z","links":{"resolver":"https://pith.science/pith/DSHOOKFAY3I53KZSFJU6LW3IXN","bundle":"https://pith.science/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/bundle.json","state":"https://pith.science/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DSHOOKFAY3I53KZSFJU6LW3IXN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:DSHOOKFAY3I53KZSFJU6LW3IXN","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":"9feef4b0cc38c39439d122cdf5c399673916185a149673d9e175091ffa2290b0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-19T19:13:26Z","title_canon_sha256":"25df62628db38c70b3444af500d6ec0140bea3558d26f97ad48947b0de56bc25"},"schema_version":"1.0","source":{"id":"1710.07324","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.07324","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"arxiv_version","alias_value":"1710.07324v2","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.07324","created_at":"2026-05-18T00:25:41Z"},{"alias_kind":"pith_short_12","alias_value":"DSHOOKFAY3I5","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_16","alias_value":"DSHOOKFAY3I53KZS","created_at":"2026-05-18T12:31:12Z"},{"alias_kind":"pith_short_8","alias_value":"DSHOOKFA","created_at":"2026-05-18T12:31:12Z"}],"graph_snapshots":[{"event_id":"sha256:0ff35bb0b0e8bc0177ddcfd5070bb465b06a651226b7361848dc34fd8ec124c5","target":"graph","created_at":"2026-05-18T00:25:41Z","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 propose a method (TT-GP) for approximate inference in Gaussian Process (GP) models. We build on previous scalable GP research including stochastic variational inference based on inducing inputs, kernel interpolation, and structure exploiting algebra. The key idea of our method is to use Tensor Train decomposition for variational parameters, which allows us to train GPs with billions of inducing inputs and achieve state-of-the-art results on several benchmarks. Further, our approach allows for training kernels based on deep neural networks without any modifications to the underlying GP model","authors_text":"Alexander Novikov, Dmitry Kropotov, Pavel Izmailov","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-19T19:13:26Z","title":"Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.07324","kind":"arxiv","version":2},"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":"sha256:2a727fb3a9e416c9c875c477132e7d177f535152d75a24605604efd8e14e852d","target":"record","created_at":"2026-05-18T00:25:41Z","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":"9feef4b0cc38c39439d122cdf5c399673916185a149673d9e175091ffa2290b0","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-10-19T19:13:26Z","title_canon_sha256":"25df62628db38c70b3444af500d6ec0140bea3558d26f97ad48947b0de56bc25"},"schema_version":"1.0","source":{"id":"1710.07324","kind":"arxiv","version":2}},"canonical_sha256":"1c8ee728a0c6d1ddab322a69e5db68bb6aeb8d6738e49bc384f1e9eeb8f66cfb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1c8ee728a0c6d1ddab322a69e5db68bb6aeb8d6738e49bc384f1e9eeb8f66cfb","first_computed_at":"2026-05-18T00:25:41.651992Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:41.651992Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kilCe5MgKBnncGRRn5rOAsK/wL2A+U2ptEs1Hj7TKRLeMhBw5eiJunzav87NK3BPOnOMkL+5T4p2N2NfTwgVCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:41.652577Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.07324","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2a727fb3a9e416c9c875c477132e7d177f535152d75a24605604efd8e14e852d","sha256:0ff35bb0b0e8bc0177ddcfd5070bb465b06a651226b7361848dc34fd8ec124c5"],"state_sha256":"6328473aab47323a1b1861acdd8536850b60585ef7e622f04837b44205746cf4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1Tw+s2DVvpDaXbRSQKOlm+n3wyMf2bAk4kMeQi8+HjW6ZK932NUrmgWh5UOT5mcye3CO3ANGGsDEj7x+6aAiBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T16:04:50.520017Z","bundle_sha256":"9b7ecf622c327d4b2338f390e81c18f8bd9130beb36a4501f2e411461270a084"}}