{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:MAFHNYSI4BGLZORS4746UPNUL5","short_pith_number":"pith:MAFHNYSI","canonical_record":{"source":{"id":"2310.02250","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T17:53:43Z","cross_cats_sorted":[],"title_canon_sha256":"0e115c985828be11eadacf41f5938d307cdfbf0ba23319959ab95df69e82efed","abstract_canon_sha256":"1b996799f25ff14ec536b1591824441bc41e399e9af502940e16b8528da7a192"},"schema_version":"1.0"},"canonical_sha256":"600a76e248e04cbcba32e7f9ea3db45f4f0157e6da60643d32325b99a5577e0e","source":{"kind":"arxiv","id":"2310.02250","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02250","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02250v3","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02250","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_12","alias_value":"MAFHNYSI4BGL","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_16","alias_value":"MAFHNYSI4BGLZORS","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_8","alias_value":"MAFHNYSI","created_at":"2026-07-05T07:46:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:MAFHNYSI4BGLZORS4746UPNUL5","target":"record","payload":{"canonical_record":{"source":{"id":"2310.02250","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T17:53:43Z","cross_cats_sorted":[],"title_canon_sha256":"0e115c985828be11eadacf41f5938d307cdfbf0ba23319959ab95df69e82efed","abstract_canon_sha256":"1b996799f25ff14ec536b1591824441bc41e399e9af502940e16b8528da7a192"},"schema_version":"1.0"},"canonical_sha256":"600a76e248e04cbcba32e7f9ea3db45f4f0157e6da60643d32325b99a5577e0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:46:23.719624Z","signature_b64":"lq2cXeYVRkQZNBWIQYc0+/3u7av/ITEqQXpBWFXrX4vHeW/IbZwGQnHn+Gb9inHhd6sr+NfvNNQKJgCUMdQWBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"600a76e248e04cbcba32e7f9ea3db45f4f0157e6da60643d32325b99a5577e0e","last_reissued_at":"2026-07-05T07:46:23.719214Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:46:23.719214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.02250","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":"2026-07-05T07:46:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O4fneH7P4QfHQ+oqoazFSt4F/TR5vsaCx8KKBDQg6eNRobZmW0sLz4f75o9/ai3kb1Gnrt6KEinGVMIjSgY0BA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T12:08:32.826600Z"},"content_sha256":"9b0f79468b5a1f47331d3fbaad840358ae7351372188699edb4ad01f07ff5b44","schema_version":"1.0","event_id":"sha256:9b0f79468b5a1f47331d3fbaad840358ae7351372188699edb4ad01f07ff5b44"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:MAFHNYSI4BGLZORS4746UPNUL5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Why should autoencoders work?","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Eduardo D. Sontag, Matthew D. Kvalheim","submitted_at":"2023-10-03T17:53:43Z","abstract_excerpt":"Deep neural network autoencoders are routinely used computationally for model reduction. They allow recognizing the intrinsic dimension of data that lie in a $k$-dimensional subset $K$ of an input Euclidean space $\\mathbb{R}^n$. The underlying idea is to obtain both an encoding layer that maps $\\mathbb{R}^n$ into $\\mathbb{R}^k$ (called the bottleneck layer or the space of latent variables) and a decoding layer that maps $\\mathbb{R}^k$ back into $\\mathbb{R}^n$, in such a way that the input data from the set $K$ is recovered when composing the two maps. This is achieved by adjusting parameters ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02250","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.02250/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T07:46:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yIn1slvTdakfh1JFJc++cRpfiycrXmFLsD9xl679Zk9yA6+bOEQ9RWC1HJGNBxHxykSuFi2vH83WgqZLJzKhBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T12:08:32.827071Z"},"content_sha256":"83ba2017bc3863e1b7a09e7d3a5f46fd7aefc8b82886378958088738a78d7a7d","schema_version":"1.0","event_id":"sha256:83ba2017bc3863e1b7a09e7d3a5f46fd7aefc8b82886378958088738a78d7a7d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MAFHNYSI4BGLZORS4746UPNUL5/bundle.json","state_url":"https://pith.science/pith/MAFHNYSI4BGLZORS4746UPNUL5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MAFHNYSI4BGLZORS4746UPNUL5/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":"2026-08-13T12:08:32Z","links":{"resolver":"https://pith.science/pith/MAFHNYSI4BGLZORS4746UPNUL5","bundle":"https://pith.science/pith/MAFHNYSI4BGLZORS4746UPNUL5/bundle.json","state":"https://pith.science/pith/MAFHNYSI4BGLZORS4746UPNUL5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MAFHNYSI4BGLZORS4746UPNUL5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:MAFHNYSI4BGLZORS4746UPNUL5","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":"1b996799f25ff14ec536b1591824441bc41e399e9af502940e16b8528da7a192","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T17:53:43Z","title_canon_sha256":"0e115c985828be11eadacf41f5938d307cdfbf0ba23319959ab95df69e82efed"},"schema_version":"1.0","source":{"id":"2310.02250","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.02250","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"arxiv_version","alias_value":"2310.02250v3","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.02250","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_12","alias_value":"MAFHNYSI4BGL","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_16","alias_value":"MAFHNYSI4BGLZORS","created_at":"2026-07-05T07:46:23Z"},{"alias_kind":"pith_short_8","alias_value":"MAFHNYSI","created_at":"2026-07-05T07:46:23Z"}],"graph_snapshots":[{"event_id":"sha256:83ba2017bc3863e1b7a09e7d3a5f46fd7aefc8b82886378958088738a78d7a7d","target":"graph","created_at":"2026-07-05T07:46:23Z","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/2310.02250/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep neural network autoencoders are routinely used computationally for model reduction. They allow recognizing the intrinsic dimension of data that lie in a $k$-dimensional subset $K$ of an input Euclidean space $\\mathbb{R}^n$. The underlying idea is to obtain both an encoding layer that maps $\\mathbb{R}^n$ into $\\mathbb{R}^k$ (called the bottleneck layer or the space of latent variables) and a decoding layer that maps $\\mathbb{R}^k$ back into $\\mathbb{R}^n$, in such a way that the input data from the set $K$ is recovered when composing the two maps. This is achieved by adjusting parameters (","authors_text":"Eduardo D. Sontag, Matthew D. Kvalheim","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T17:53:43Z","title":"Why should autoencoders work?"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.02250","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":"sha256:9b0f79468b5a1f47331d3fbaad840358ae7351372188699edb4ad01f07ff5b44","target":"record","created_at":"2026-07-05T07:46:23Z","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":"1b996799f25ff14ec536b1591824441bc41e399e9af502940e16b8528da7a192","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-10-03T17:53:43Z","title_canon_sha256":"0e115c985828be11eadacf41f5938d307cdfbf0ba23319959ab95df69e82efed"},"schema_version":"1.0","source":{"id":"2310.02250","kind":"arxiv","version":3}},"canonical_sha256":"600a76e248e04cbcba32e7f9ea3db45f4f0157e6da60643d32325b99a5577e0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"600a76e248e04cbcba32e7f9ea3db45f4f0157e6da60643d32325b99a5577e0e","first_computed_at":"2026-07-05T07:46:23.719214Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:46:23.719214Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lq2cXeYVRkQZNBWIQYc0+/3u7av/ITEqQXpBWFXrX4vHeW/IbZwGQnHn+Gb9inHhd6sr+NfvNNQKJgCUMdQWBw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:46:23.719624Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.02250","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9b0f79468b5a1f47331d3fbaad840358ae7351372188699edb4ad01f07ff5b44","sha256:83ba2017bc3863e1b7a09e7d3a5f46fd7aefc8b82886378958088738a78d7a7d"],"state_sha256":"1f999c7b06b6b2ca7cccef1e4160f3234edcf3c720860daa54048ceb25155245"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4DlipaEbua9hG5bmfcWLNvevmbVApft+BTa68LF5eiCn2/14F+f7hxtyNTvSCeAD+nlGCQnwPY+sI2HVnBsVBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T12:08:32.855001Z","bundle_sha256":"ccbb3aa947f808056ea0dd0861a4ea4e7dfae740a964a789b544169e6d1b0628"}}