{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:3L2TPYSH54YTURJZJBGBBM6N37","short_pith_number":"pith:3L2TPYSH","canonical_record":{"source":{"id":"1806.00499","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T18:28:20Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"06fa4f29bb4b19d9607fec4c6718e55cbd2edb70acc97ef8be3fb3a6705191a7","abstract_canon_sha256":"0bb691378b7e06d9c72898b2f89bf30b1d2785514dffd18254cec8fa1acc32d4"},"schema_version":"1.0"},"canonical_sha256":"daf537e247ef313a4539484c10b3cddfebae57bd454fe5a9bf9e29ac9d90f930","source":{"kind":"arxiv","id":"1806.00499","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00499","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00499v1","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00499","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"pith_short_12","alias_value":"3L2TPYSH54YT","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3L2TPYSH54YTURJZ","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3L2TPYSH","created_at":"2026-05-18T12:32:02Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:3L2TPYSH54YTURJZJBGBBM6N37","target":"record","payload":{"canonical_record":{"source":{"id":"1806.00499","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T18:28:20Z","cross_cats_sorted":["cs.AI","stat.ML"],"title_canon_sha256":"06fa4f29bb4b19d9607fec4c6718e55cbd2edb70acc97ef8be3fb3a6705191a7","abstract_canon_sha256":"0bb691378b7e06d9c72898b2f89bf30b1d2785514dffd18254cec8fa1acc32d4"},"schema_version":"1.0"},"canonical_sha256":"daf537e247ef313a4539484c10b3cddfebae57bd454fe5a9bf9e29ac9d90f930","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:21.054353Z","signature_b64":"M/F8YIeTFBLPLRkU8AuEvI80goyFIBVoaYmASIVe6gKJlcyvYx64EtFa5LJoKqb6FwUDcaowGCgQElAY54U+BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"daf537e247ef313a4539484c10b3cddfebae57bd454fe5a9bf9e29ac9d90f930","last_reissued_at":"2026-05-18T00:14:21.053775Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:21.053775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.00499","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":"2026-05-18T00:14:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"a9CeVWsOGTOKK352UwTWYmwihP9ncGBTbRB3ftQe5oaLfW8aW8FTAV7mehhlUkxMW31LY7/WQ7Dw/+eLE0BqCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T08:06:58.393091Z"},"content_sha256":"53d677fcb25473276e0b271514c04836cd59a84cb53475abe68df644956d7593","schema_version":"1.0","event_id":"sha256:53d677fcb25473276e0b271514c04836cd59a84cb53475abe68df644956d7593"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:3L2TPYSH54YTURJZJBGBBM6N37","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Backpropagation for Implicit Spectral Densities","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","stat.ML"],"primary_cat":"cs.LG","authors_text":"Aditya Ramesh, Yann LeCun","submitted_at":"2018-06-01T18:28:20Z","abstract_excerpt":"Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data when explicit goals cannot be provided. These unsupervised systems are commonly trained by backpropagating through a likelihood function. We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model. Explicitly defining the likelihood often entails making heavy-handed assumptions that impede our ability to solve challenging tasks. On"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00499","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":"2026-05-18T00:14:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RII3v5HKUoljGS94GCdv7qcdmoejvWuYznm9qj/HmkeX/7xtSmjiiXUyItqKhEHRGk61PBa03q4CoevfY8r5AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T08:06:58.393854Z"},"content_sha256":"cb6b9db70a51476857956d5fbcf4cdccaa6b4884f7bc26e4acb92775f2c183c5","schema_version":"1.0","event_id":"sha256:cb6b9db70a51476857956d5fbcf4cdccaa6b4884f7bc26e4acb92775f2c183c5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3L2TPYSH54YTURJZJBGBBM6N37/bundle.json","state_url":"https://pith.science/pith/3L2TPYSH54YTURJZJBGBBM6N37/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3L2TPYSH54YTURJZJBGBBM6N37/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":"2026-06-08T08:06:58Z","links":{"resolver":"https://pith.science/pith/3L2TPYSH54YTURJZJBGBBM6N37","bundle":"https://pith.science/pith/3L2TPYSH54YTURJZJBGBBM6N37/bundle.json","state":"https://pith.science/pith/3L2TPYSH54YTURJZJBGBBM6N37/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3L2TPYSH54YTURJZJBGBBM6N37/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:3L2TPYSH54YTURJZJBGBBM6N37","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":"0bb691378b7e06d9c72898b2f89bf30b1d2785514dffd18254cec8fa1acc32d4","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T18:28:20Z","title_canon_sha256":"06fa4f29bb4b19d9607fec4c6718e55cbd2edb70acc97ef8be3fb3a6705191a7"},"schema_version":"1.0","source":{"id":"1806.00499","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.00499","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"arxiv_version","alias_value":"1806.00499v1","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.00499","created_at":"2026-05-18T00:14:21Z"},{"alias_kind":"pith_short_12","alias_value":"3L2TPYSH54YT","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_16","alias_value":"3L2TPYSH54YTURJZ","created_at":"2026-05-18T12:32:02Z"},{"alias_kind":"pith_short_8","alias_value":"3L2TPYSH","created_at":"2026-05-18T12:32:02Z"}],"graph_snapshots":[{"event_id":"sha256:cb6b9db70a51476857956d5fbcf4cdccaa6b4884f7bc26e4acb92775f2c183c5","target":"graph","created_at":"2026-05-18T00:14:21Z","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":"Most successful machine intelligence systems rely on gradient-based learning, which is made possible by backpropagation. Some systems are designed to aid us in interpreting data when explicit goals cannot be provided. These unsupervised systems are commonly trained by backpropagating through a likelihood function. We introduce a tool that allows us to do this even when the likelihood is not explicitly set, by instead using the implicit likelihood of the model. Explicitly defining the likelihood often entails making heavy-handed assumptions that impede our ability to solve challenging tasks. On","authors_text":"Aditya Ramesh, Yann LeCun","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T18:28:20Z","title":"Backpropagation for Implicit Spectral Densities"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.00499","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":"sha256:53d677fcb25473276e0b271514c04836cd59a84cb53475abe68df644956d7593","target":"record","created_at":"2026-05-18T00:14:21Z","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":"0bb691378b7e06d9c72898b2f89bf30b1d2785514dffd18254cec8fa1acc32d4","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2018-06-01T18:28:20Z","title_canon_sha256":"06fa4f29bb4b19d9607fec4c6718e55cbd2edb70acc97ef8be3fb3a6705191a7"},"schema_version":"1.0","source":{"id":"1806.00499","kind":"arxiv","version":1}},"canonical_sha256":"daf537e247ef313a4539484c10b3cddfebae57bd454fe5a9bf9e29ac9d90f930","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"daf537e247ef313a4539484c10b3cddfebae57bd454fe5a9bf9e29ac9d90f930","first_computed_at":"2026-05-18T00:14:21.053775Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:21.053775Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M/F8YIeTFBLPLRkU8AuEvI80goyFIBVoaYmASIVe6gKJlcyvYx64EtFa5LJoKqb6FwUDcaowGCgQElAY54U+BA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:21.054353Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.00499","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:53d677fcb25473276e0b271514c04836cd59a84cb53475abe68df644956d7593","sha256:cb6b9db70a51476857956d5fbcf4cdccaa6b4884f7bc26e4acb92775f2c183c5"],"state_sha256":"f88e419679b65ebbd2fc0dbaa64102cc0b57783729a4ab211701525bd537a2bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"90R/aOdhXy7knTEuc8LNtvNb7L7Z9EkZIz4mVYC/1NLrS5HWjvKrT3Iq44glOYH0LzjbaN/MnQLEY8y5vYZaDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T08:06:58.397538Z","bundle_sha256":"4df42c50a93d7b6dfe63fb0fba99be64b75620ff4f9281161eb1906d31bd2a58"}}