{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:FRWKTUGDKHC7EKIODB4DPZFH5G","short_pith_number":"pith:FRWKTUGD","canonical_record":{"source":{"id":"1406.3269","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-12T15:40:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1d6b877f820ae85aa9d6b8fbe41945cd57452b02ef290f1d1334f3339d0b8b66","abstract_canon_sha256":"0c009f61f42d05c17ccf682262267e678dabbe4f6086d574145c05357a4defd5"},"schema_version":"1.0"},"canonical_sha256":"2c6ca9d0c351c5f2290e187837e4a7e9ba32e91400d55197a0c604bf80f39788","source":{"kind":"arxiv","id":"1406.3269","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.3269","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"arxiv_version","alias_value":"1406.3269v3","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.3269","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"pith_short_12","alias_value":"FRWKTUGDKHC7","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FRWKTUGDKHC7EKIO","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FRWKTUGD","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:FRWKTUGDKHC7EKIODB4DPZFH5G","target":"record","payload":{"canonical_record":{"source":{"id":"1406.3269","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-12T15:40:18Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1d6b877f820ae85aa9d6b8fbe41945cd57452b02ef290f1d1334f3339d0b8b66","abstract_canon_sha256":"0c009f61f42d05c17ccf682262267e678dabbe4f6086d574145c05357a4defd5"},"schema_version":"1.0"},"canonical_sha256":"2c6ca9d0c351c5f2290e187837e4a7e9ba32e91400d55197a0c604bf80f39788","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:19:06.015220Z","signature_b64":"Mb2M9vwNP5Nirg9ftxfe8C4Rfidxwc/KjNSPFKR377U3XgzEW44C+yw9Dd+kB8q0G9duRDLKMPl175UsHppgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c6ca9d0c351c5f2290e187837e4a7e9ba32e91400d55197a0c604bf80f39788","last_reissued_at":"2026-05-18T02:19:06.014812Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:19:06.014812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1406.3269","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:19:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SEmJ8yHZeqA8KYMPDqSry1GX05tdVatf4x+8BEl2uyEMKFhYkRpY6kDsyAwC0sO4rwilqyTEuLNWnuxFteOsBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T03:05:16.295943Z"},"content_sha256":"ee87b095633dc29a9ac830716c2fcaf9fc350859f58242d65a1ce4b8df78eb7e","schema_version":"1.0","event_id":"sha256:ee87b095633dc29a9ac830716c2fcaf9fc350859f58242d65a1ce4b8df78eb7e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:FRWKTUGDKHC7EKIODB4DPZFH5G","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scheduled denoising autoencoders","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Charles Sutton, Krzysztof J. Geras","submitted_at":"2014-06-12T15:40:18Z","abstract_excerpt":"We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the network tends to learn fine-grained features. This motivates the scheduled denoising autoencoder, which starts with a high level of noise that lowers as training progresses. We find that the resulting representation yields a significant boost on a lat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.3269","kind":"arxiv","version":3},"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":"2026-05-18T02:19:06Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U9FTWWpyvbq8sCnbCg2eZM4E/w2uCsXqFLdnc7kzA5xrTHu3REfijIhUhYuANxr6gbalEqS1FW6y0BKlI4g9AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T03:05:16.296414Z"},"content_sha256":"e336b11f387b925aad86ac594597ed63a03f83e4f3e9c875eefc284d350b958f","schema_version":"1.0","event_id":"sha256:e336b11f387b925aad86ac594597ed63a03f83e4f3e9c875eefc284d350b958f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/bundle.json","state_url":"https://pith.science/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/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":"2026-05-19T03:05:16Z","links":{"resolver":"https://pith.science/pith/FRWKTUGDKHC7EKIODB4DPZFH5G","bundle":"https://pith.science/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/bundle.json","state":"https://pith.science/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FRWKTUGDKHC7EKIODB4DPZFH5G/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:FRWKTUGDKHC7EKIODB4DPZFH5G","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":"0c009f61f42d05c17ccf682262267e678dabbe4f6086d574145c05357a4defd5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-12T15:40:18Z","title_canon_sha256":"1d6b877f820ae85aa9d6b8fbe41945cd57452b02ef290f1d1334f3339d0b8b66"},"schema_version":"1.0","source":{"id":"1406.3269","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1406.3269","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"arxiv_version","alias_value":"1406.3269v3","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1406.3269","created_at":"2026-05-18T02:19:06Z"},{"alias_kind":"pith_short_12","alias_value":"FRWKTUGDKHC7","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"FRWKTUGDKHC7EKIO","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"FRWKTUGD","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:e336b11f387b925aad86ac594597ed63a03f83e4f3e9c875eefc284d350b958f","target":"graph","created_at":"2026-05-18T02:19:06Z","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 representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the network tends to learn fine-grained features. This motivates the scheduled denoising autoencoder, which starts with a high level of noise that lowers as training progresses. We find that the resulting representation yields a significant boost on a lat","authors_text":"Charles Sutton, Krzysztof J. Geras","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-12T15:40:18Z","title":"Scheduled denoising autoencoders"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1406.3269","kind":"arxiv","version":3},"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:ee87b095633dc29a9ac830716c2fcaf9fc350859f58242d65a1ce4b8df78eb7e","target":"record","created_at":"2026-05-18T02:19:06Z","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":"0c009f61f42d05c17ccf682262267e678dabbe4f6086d574145c05357a4defd5","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2014-06-12T15:40:18Z","title_canon_sha256":"1d6b877f820ae85aa9d6b8fbe41945cd57452b02ef290f1d1334f3339d0b8b66"},"schema_version":"1.0","source":{"id":"1406.3269","kind":"arxiv","version":3}},"canonical_sha256":"2c6ca9d0c351c5f2290e187837e4a7e9ba32e91400d55197a0c604bf80f39788","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2c6ca9d0c351c5f2290e187837e4a7e9ba32e91400d55197a0c604bf80f39788","first_computed_at":"2026-05-18T02:19:06.014812Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:19:06.014812Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mb2M9vwNP5Nirg9ftxfe8C4Rfidxwc/KjNSPFKR377U3XgzEW44C+yw9Dd+kB8q0G9duRDLKMPl175UsHppgBg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:19:06.015220Z","signed_message":"canonical_sha256_bytes"},"source_id":"1406.3269","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ee87b095633dc29a9ac830716c2fcaf9fc350859f58242d65a1ce4b8df78eb7e","sha256:e336b11f387b925aad86ac594597ed63a03f83e4f3e9c875eefc284d350b958f"],"state_sha256":"abb8af717b9f1160beb9c5d25ecba3fd20de2d6870011e3e6e580b292b3b672d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9ud04WQ+AJqfozBD7jEw5rGoaMlutRkehssuFtcnGYYcyLrGpk5HInc5yXTuvz/VTLnSPSf4Mv+Zl4m2djEtBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T03:05:16.298301Z","bundle_sha256":"e3a049fdfbce191c7fa79741f9323881e82356a1b7495f640ad9a04768ba1ebf"}}