{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:2Q5UTLTEAVMV346L6HJRFXBKTC","short_pith_number":"pith:2Q5UTLTE","canonical_record":{"source":{"id":"1212.2460","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-19T15:05:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4adcb347af4e761eef5b43c5a1fbc2259d14a3d3d0d2616509f948efbc48be25","abstract_canon_sha256":"6cb8e9c4b50cbf5c0ff53d2c2a79ffeaf10acd1cf2a0da271fdca76654b33b20"},"schema_version":"1.0"},"canonical_sha256":"d43b49ae6405595df3cbf1d312dc2a98b653ef46dedbebf5a12366480d29aa75","source":{"kind":"arxiv","id":"1212.2460","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.2460","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"1212.2460v1","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.2460","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"2Q5UTLTEAVMV","created_at":"2026-05-18T12:26:50Z"},{"alias_kind":"pith_short_16","alias_value":"2Q5UTLTEAVMV346L","created_at":"2026-05-18T12:26:50Z"},{"alias_kind":"pith_short_8","alias_value":"2Q5UTLTE","created_at":"2026-05-18T12:26:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:2Q5UTLTEAVMV346L6HJRFXBKTC","target":"record","payload":{"canonical_record":{"source":{"id":"1212.2460","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-19T15:05:02Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4adcb347af4e761eef5b43c5a1fbc2259d14a3d3d0d2616509f948efbc48be25","abstract_canon_sha256":"6cb8e9c4b50cbf5c0ff53d2c2a79ffeaf10acd1cf2a0da271fdca76654b33b20"},"schema_version":"1.0"},"canonical_sha256":"d43b49ae6405595df3cbf1d312dc2a98b653ef46dedbebf5a12366480d29aa75","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:38:44.822566Z","signature_b64":"HYibVrMwSb7/xObAXTgovcTlyHhLoaJAq5Hx6OwlmLWHexYE68Nhveh1lkMLZiT33mhEoip42Ehb4+XwLCiaAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d43b49ae6405595df3cbf1d312dc2a98b653ef46dedbebf5a12366480d29aa75","last_reissued_at":"2026-05-18T03:38:44.822104Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:38:44.822104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1212.2460","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":"2026-05-18T03:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HO4DAa16aZji/jTvJe0wzhF4pdZR9vWEAGPvRypBFwuYznvwNEnONkl7SnsQQPAMoejpQA/vYaT2y9nItfGxAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T04:19:15.122117Z"},"content_sha256":"1c094c37c4f78850f6ae271bde5108449f14eee7048216e6ad4f68f4b3ee770b","schema_version":"1.0","event_id":"sha256:1c094c37c4f78850f6ae271bde5108449f14eee7048216e6ad4f68f4b3ee770b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:2Q5UTLTEAVMV346L6HJRFXBKTC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"The Information Bottleneck EM Algorithm","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Gal Elidan, Nir Friedman","submitted_at":"2012-10-19T15:05:02Z","abstract_excerpt":"Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle.  In this approach, we view the learning problem as a tradeoff between two information theoretic objectives. The first is to make the hidden variables uninformative about the identity of specific instances. The second is t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2460","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":"2026-05-18T03:38:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yP2OUj2y88rWCtViqI8gjkjgzKxrFz5UsfhZ2gnPChDIDBjZwnXfLAktfgFd/Yl2cynjn0lqhsZ0d6JiO4EKAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T04:19:15.122487Z"},"content_sha256":"b81ad8d8d77ba8224040823928b8e729ff938e6ff112019e1701c3e651b7a6c6","schema_version":"1.0","event_id":"sha256:b81ad8d8d77ba8224040823928b8e729ff938e6ff112019e1701c3e651b7a6c6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/bundle.json","state_url":"https://pith.science/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/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":"2026-06-12T04:19:15Z","links":{"resolver":"https://pith.science/pith/2Q5UTLTEAVMV346L6HJRFXBKTC","bundle":"https://pith.science/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/bundle.json","state":"https://pith.science/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2Q5UTLTEAVMV346L6HJRFXBKTC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:2Q5UTLTEAVMV346L6HJRFXBKTC","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":"6cb8e9c4b50cbf5c0ff53d2c2a79ffeaf10acd1cf2a0da271fdca76654b33b20","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-19T15:05:02Z","title_canon_sha256":"4adcb347af4e761eef5b43c5a1fbc2259d14a3d3d0d2616509f948efbc48be25"},"schema_version":"1.0","source":{"id":"1212.2460","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1212.2460","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"arxiv_version","alias_value":"1212.2460v1","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.2460","created_at":"2026-05-18T03:38:44Z"},{"alias_kind":"pith_short_12","alias_value":"2Q5UTLTEAVMV","created_at":"2026-05-18T12:26:50Z"},{"alias_kind":"pith_short_16","alias_value":"2Q5UTLTEAVMV346L","created_at":"2026-05-18T12:26:50Z"},{"alias_kind":"pith_short_8","alias_value":"2Q5UTLTE","created_at":"2026-05-18T12:26:50Z"}],"graph_snapshots":[{"event_id":"sha256:b81ad8d8d77ba8224040823928b8e729ff938e6ff112019e1701c3e651b7a6c6","target":"graph","created_at":"2026-05-18T03:38:44Z","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":"Learning with hidden variables is a central challenge in probabilistic graphical models that has important implications for many real-life problems. The classical approach is using the Expectation Maximization (EM) algorithm. This algorithm, however, can get trapped in local maxima. In this paper we explore a new approach that is based on the Information Bottleneck principle.  In this approach, we view the learning problem as a tradeoff between two information theoretic objectives. The first is to make the hidden variables uninformative about the identity of specific instances. The second is t","authors_text":"Gal Elidan, Nir Friedman","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-19T15:05:02Z","title":"The Information Bottleneck EM Algorithm"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2460","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":"sha256:1c094c37c4f78850f6ae271bde5108449f14eee7048216e6ad4f68f4b3ee770b","target":"record","created_at":"2026-05-18T03:38:44Z","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":"6cb8e9c4b50cbf5c0ff53d2c2a79ffeaf10acd1cf2a0da271fdca76654b33b20","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2012-10-19T15:05:02Z","title_canon_sha256":"4adcb347af4e761eef5b43c5a1fbc2259d14a3d3d0d2616509f948efbc48be25"},"schema_version":"1.0","source":{"id":"1212.2460","kind":"arxiv","version":1}},"canonical_sha256":"d43b49ae6405595df3cbf1d312dc2a98b653ef46dedbebf5a12366480d29aa75","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d43b49ae6405595df3cbf1d312dc2a98b653ef46dedbebf5a12366480d29aa75","first_computed_at":"2026-05-18T03:38:44.822104Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:38:44.822104Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HYibVrMwSb7/xObAXTgovcTlyHhLoaJAq5Hx6OwlmLWHexYE68Nhveh1lkMLZiT33mhEoip42Ehb4+XwLCiaAg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:38:44.822566Z","signed_message":"canonical_sha256_bytes"},"source_id":"1212.2460","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1c094c37c4f78850f6ae271bde5108449f14eee7048216e6ad4f68f4b3ee770b","sha256:b81ad8d8d77ba8224040823928b8e729ff938e6ff112019e1701c3e651b7a6c6"],"state_sha256":"2eedc4a8fa94a6dd34eeedc465fe7f8218d938b0b6b2155e16ba31fd5e8dc49d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nqW1YiaVmXdc0KBnt/814bA1GjNXOhxn2MUmg9+qxzigDLAsWdBUtmgpuSnyOQEGWHgNAiNEdtuXsiutaOl9Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T04:19:15.124408Z","bundle_sha256":"307940203858b7f3fb8be346cc39b43a3d8cab7fa2b3e10a4edc80b6c81a2f15"}}